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In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush–Kuhn–Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems. [ABSTRACT FROM AUTHOR]

Using reinforcement learning (RL) algorithm to optimize guidance law can address non-idealities in complex environment. However, the optimization is difficult due to huge state-action space, unstable training, and high requirements on expertise. In this paper, the constrained guidance policy of a neural guidance system is optimized using improved RL algorithm, which is motivated by the idea of traditional model-based guidance method. A novel optimization objective with minimum overload regularization is developed to restrain the guidance policy directly from generating redundant missile maneuver. Moreover, a bi-level curriculum learning is designed to facilitate the policy optimization. Experiment results show that the proposed minimum overload regularization can reduce the vertical overloads of missile significantly, and the bi-level curriculum learning can further accelerate the optimization of guidance policy. [ABSTRACT FROM AUTHOR]

Residual block is a very common component in recent state-of-the art CNNs such as EfficientNet/EfficientDet. Shortcut data accounts for nearly 40% of feature-maps access in ResNet152. Most of the previous DNN compilers/accelerators ignore the shortcut data optimization. This paper presents ShortcutFusion, an optimization tool for FPGA-based accelerator with a reuse-aware static memory allocation for shortcut data, to maximize on-chip data reuse given resource constraints. From TensorFlow DNN models, the proposed design generates instruction sets for a group of nodes which uses an optimized data reuse for each residual block. The accelerator design implemented on the Xilinx KCU1500 FPGA card $2.8\times $ faster and $9.9\times $ more power efficient than NVIDIA RTX 2080 Ti for $256\times 256$ input size. Compared to the result from baseline, in which the weights/inputs/outputs are accessed from the off-chip memory exactly once per each layer, ShortcutFusion reduces the DRAM access by 47.8-84.8% for RetinaNet, Yolov3, ResNet152, and EfficientNet. Given a similar buffer size to ShortcutMining, which also “mine” the shortcut data in hardware, the proposed work reduces off-chip access for feature-maps $5.27\times $ while accessing weight from off-chip memory exactly once. [ABSTRACT FROM AUTHOR]

The main focus of this paper is the presentation of reliable methods for the determination of the optimum coloring of a graph, commonly known in the literature as vertex coloring problem. It has been shown that networks of capacitively coupled oscillators can be used to solve vertex coloring problems. In this paper we address the negative impact of an unbalanced number of couplings for the oscillators on the performance of the network and compensate for this non-uniform coupling structure by an adjustment in the network itself. The negative effect of the memristor device-to-device variability of the NbOx memristor on the array functionality will be investigated and reduced via an adaptation of the memristor operating point. The main improvement in network performance is achieved by setting up a control procedure allowing the network to bypass the local solutions and converge to the global one. Two strategies inspired by global optimization algorithms will be proposed to allow the network to overcome sub-optimal solutions, and find the solution corresponding to the absolute minimum of a performance measure function of the vertex coloring problem. [ABSTRACT FROM AUTHOR]

Setting retail electricity prices is one of the significant strategies for energy management of multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of privacy preservation, the uncertainties of renewable energy and loads, as well as the time-varying scenarios, bring challenges for pricing problems. In this paper, a two-level pricing framework is proposed based on interval predictions and model-free reinforcement learning to address these challenges. In particular, at the higher level, the distribution system operator (DSO) is viewed as an agent, which sets retail electricity prices without detailed user information for privacy protection to maximize the total revenue from selling energy with reinforcement learning. For time-varying scenarios with intermittent photovoltaic power generation and diverse loads, a differentiable trust region layer is considered in reinforcement learning to improve the robustness of the policy updating process. While at the lower level, operators in microgrids solve three-phase unbalanced optimal power flow (OPF) problems to minimize generation cost and network power loss. Additionally, to deal with the challenges from the uncertainties of renewable power generation and user loads, interval predictions are chosen to quantify prediction errors and improve the flexibility of pricing policies. Finally, a set of experiments are conducted to validate the effectiveness of the proposed method for pricing problems in MMG systems. [ABSTRACT FROM AUTHOR]

For the design of ring oscillator-based ADCs, little has been reported on how to optimally co-design the readout scheme and the ring oscillator core towards optimal energy efficiency. This paper describes a methodology to find this optimum for a target SQNR and signal bandwidth, for the case of 1st-order quantization noise shaping. In short, starting from initial assumptions on the VCO, the readout optimization boils down to finding the best VCO frequency. From this, the number of readout phases, the sampling frequency of the readout and the number of bits in the quantizers are derived. Then, it is explained how to adjust the VCO core toward the corresponding optimal VCO configuration. This is discussed for the case of a current-controlled ring oscillator, indicating the main constraints that define the design space for such a VCO optimization. If the actual VCO configuration is bounded by practical constraints, this approach can be re-iterated where a new readout optimization is performed taking these constraints into account. [ABSTRACT FROM AUTHOR]

This paper deals with the frequency design of lossless passive electronic filters under magnitude constraints. With the huge increase in design complexity for mobile applications, new systematic and efficient methods are required. This paper focuses on the direct synthesis approach, an historical design approach that has not been recently updated. It consists in directly synthesizing the $LC$ values of a pre-specified circuit until the spectral mask is satisfied. While beneficial in practice, this approach typically leads to an important computational time and requires an initial guess to reduce it. Based on recent developments of the System and Control community, that led to efficient methods for system design, the direct synthesis approach is revisited. To achieve this, the port-Hamiltonian Differential Algebraic Equation (pHDAE) representation, that particularly fits the design problem, is introduced. A synthesis method is then developed, leading to solve an optimization problem of moderate complexity. For particular cases, this complexity happens to be remarkably low. Based on this observation, a second method reveals how to obtain such complexity for the more general case, using an original combination between the pHDAE and the LFT representations. Finally, a numerical example shows the validity and illustrates the benefits of this work. [ABSTRACT FROM AUTHOR]

The memristor-based neuromorphic computing system (NCS) with emerging storage and computing integration architecture has drawn extensive attention. Because of the unique nonvolatility and programmability, the memristor is an ideal nano-device to realize neural synapses in VLSI circuit implementation of neural networks. However, in the hardware implementation, the performance of the memristive neural network is always affected by quantization error, writing error, and conductance drift, which seriously hinders its applications in practice. In this paper, a novel weight optimization scheme combining quantization and Bayesian inference is proposed to alleviate this problem. Specifically, the weight deviation in the memristive neural network is transformed into the weight uncertainty in the Bayesian neural network, which can make the network insensitive to unexpected weight changes. A quantization regularization term is designed and utilized during the training process of the Bayesian neural network, reducing the quantization error and improving the robustness of the network. Furthermore, a partial training method is raised to extend the applicability of the proposed scheme in large-scale neural networks. Finally, the experiments on a Multilayer Perceptron and LeNet demonstrate that the proposed weight optimization scheme can significantly enhance the robustness of memristive neural networks. [ABSTRACT FROM AUTHOR]

In this paper, we revisit Boole’s expansion theorem to propose a new synthesis method for implication logic circuits based on memristors. By rewriting the sum-of-products form of Boole’s expansion theorem in terms that are best suited for the implication logic, we develop a generalized rule to derive the sequence of operations needed to realize any logic function written in the classical AND-OR form. The proposed method leverages on multi-input operation, minimizing both the number of steps required to compute a given Boolean function and the number of memristors involved. Moreover, it allows using well-established methods of logic circuit optimization like binary decision diagrams, Karnaugh maps, and heuristic algorithms, that are already implemented in commercial CAD software. The proposed method allows a fair comparison between the performance of CMOS and implication logic implementations of the same logic function under the same degree of optimization, and is shown to outperform existing approaches. Possible device-circuit co-design strategies to optimize circuit performance are finally discussed. [ABSTRACT FROM AUTHOR]

To better understand the correlation between network topological features and the robustness of network controllability in a general setting, this paper suggests a practical approach to searching for optimal network topologies with given numbers of nodes and edges. Since theoretical analysis seems impossible at least in the present time, exhaustive search based on optimization techniques is employed, firstly for a group of small-sized networks that are realistically workable, where exhaustive means 1) all possible network structures with the given numbers of nodes and edges are computed and compared, and 2) all possible node-removal sequences are considered. A main contribution of this paper is the observation of an empirical necessary condition (ENC) from the results of exhaustive search, which shrinks the search space to quickly find an optimal solution. ENC shows that the maximum and minimum in- and out-degrees of an optimal network structure should be almost identical, or within a very narrow range, i.e., the network should be extremely homogeneous. Edge rectification towards the satisfaction of the ENC is then designed and evaluated. Simulation results on large-sized synthetic and real-world networks verify the effectiveness of both the observed ENC and the edge rectification scheme. As more operations of edge rectification are performed, the network is getting closer to exactly satisfying the ENC, and consequently the robustness of the network controllability is enhanced towards optimum. [ABSTRACT FROM AUTHOR]

This paper presents a transimpedance-to-noise optimization approach for design of a resistive shunt-feedback TIA. This optimization offers an enhancement in the transimpedance and a noise performance very close to the theoretical minimum noise of the TIA. In addition, the transimpedance-to-noise optimization approach results in a small front-end FET size which enables a further reduction in power and area. Moreover, this approach enables using a fewer number of stages in the receiver chain which makes a high PSRR feasible and obviates the necessity for using an offset cancellation circuitry. Building on this approach, a fully differential analog front-end including a resistive shunt-feedback TIA and a post amplifier (PA) for time-of-flight (ToF) Lidar receivers is designed and implemented, achieving 94dB Ω transimpedance gain, 71nA input-referred rms noise current, −3dB bandwidth of 340MHz, and power supply rejection ratio (PSRR) of more than 87dB in a 0.11 μm CMOS process. The associated DC power consumption is 19.4mW with V DD of 1.8V. Moreover, a push-pull buffer with 1V output swing is integrated for driving 50 Ω loads, such as off-chip time discriminators, which also additionally amplifies the signal with a gain of 5dB while consuming an extra 20.9mW of DC power. The whole chip (excluding pads) occupies 210 μm × 110 μm in area. [ABSTRACT FROM AUTHOR]

Recently, there have been many studies attempting to take advantage of advancements in Artificial Intelligence (AI) in Analog and Mixed-Signal (AMS) circuit design. Automated circuit sizing optimization and improving the accuracy of performance models are the two predominant uses of AI in AMS circuit design. This paper first introduces and explains the basic concepts in AI especially the ones that are more suitable to this application. Next, it surveys some recent studies of various AI techniques for AMS circuit design. Then, it discusses the main approaches as well as the pros and cons of each method. Finally, it gives meaningful insights about the current challenges and open issues, as well as recommends approaches for specific applications. [ABSTRACT FROM AUTHOR]

Dropout and DropConnect are two techniques to facilitate the regularization of neural network models, having achieved the state-of-the-art results in several benchmarks. In this paper, to improve the generalization capability of spiking neural networks (SNNs), the two drop techniques are first applied to the state-of-the-art SpikeProp learning algorithm resulting in two improved learning algorithms called SPDO (SpikeProp with Dropout) and SPDC (SpikeProp with DropConnect). In view that a higher membrane potential of a biological neuron implies a higher probability of neural activation, three adaptive drop algorithms, SpikeProp with Adaptive Dropout (SPADO), SpikeProp with Adaptive DropConnect (SPADC), and SpikeProp with Group Adaptive Drop (SPGAD), are proposed by adaptively adjusting the keep probability for training SNNs. A convergence theorem for SPDC is proven under the assumptions of the bounded norm of connection weights and a finite number of equilibria. In addition, the five proposed algorithms are carried out in a collaborative neurodynamic optimization framework to improve the learning performance of SNNs. The experimental results on the four benchmark data sets demonstrate that the three adaptive algorithms converge faster than SpikeProp, SPDO, and SPDC, and the generalization errors of the five proposed algorithms are significantly smaller than that of SpikeProp. Furthermore, the experimental results also show that the five algorithms based on collaborative neurodynamic optimization can be improved in terms of several measures. [ABSTRACT FROM AUTHOR]

Training of Deep Neural Network (DNN) at the edge faces the challenge of high energy consumption due to the requirements of a large number of memory accesses for gradient calculations. Therefore, it is necessary to minimize data fetches to perform training of a DNN model on the edge. In this paper, a novel technique has been proposed to reduce the memory access for the training of fully connected layers in transfer learning. By analyzing the memory access patterns in the backpropagation phase in fully connected layers, the memory access can be optimized. We introduce a new method to update the weights by introducing the delta term for every node of output and fully connected layer. Delta term aims to reduce memory access for the parameters which are required to access repeatedly during the training process of fully connected layers. The proposed technique shows 0.13x-13.93x energy savings for the training of fully connected layers for famous DNN architectures on multiple processor architectures. The proposed technique can be used to perform transfer learning on-chip to reduce energy consumption as well as memory access. [ABSTRACT FROM AUTHOR]

Once there are constant or infrequently changed bits (COIBs) in two adjacent input messages of SHA-2, the switching power of input messages data registers (IMD-REGs) used for COIBs will disappear. Meanwhile, when full pipeline SHA-2 is applied in a certain application scenario where the IMD-REGs used for COIBs can be removed, more area of full pipeline SHA-2 can be saved as the proportion of IMD-REGs in message word registers increases. This paper proposes a new message expansion structure for full pipeline SHA-2 to increase the proportion of IMD-REGs. By inserting two expanders in last part of expansion structure pipeline stages and rescheduling the expander, the consumption rate of input messages will be decreased and the proportion of IMD-REGs will be increased. Compared with normal message expansion structure, the ratio of IMD-REGs to total message word registers in the proposed structure is increased from 15.1% to 41.6% for full pipeline SHA256, and 11.2% to 32.4% for full pipeline SHA512. When COIBs exists in adjacent input messages, the power and area advantages of proposed new message expansion structure have been demonstrated by FPGA and ASIC implementations. [ABSTRACT FROM AUTHOR]

Under process, voltage, and temperature variations, SRAM cell stability largely fluctuates from the nominal value. In the design step, SRAM cell optimization while ignoring the fluctuation induces the yield loss for the stability. Variation-aware optimization of an SRAM cell can prevent the yield loss problem by considering the mean and variance of SRAM cell stability when finding optimal design parameters. This paper proposes a novel SRAM optimization method that uses a deep neural network (DNN). Multiple DNNs from ensemble techniques represent the mean and variance of SRAM cell stability for the nominal design parameters. Subsequent sensitivity analysis of DNN extracts the ${K}$ design parameters that have the most dominant effects on the mean and variance of SRAM cell stability. Then multidimensional optimization is used to find the optimal values of these ${K}$ parameters to maximize the mean stability while minimizing its variance. The proposed method achieved an average of 2% error compared to MC simulation. The proposed optimization method takes only 561 s to provide the most optimal design parameter values of an SRAM cell. [ABSTRACT FROM AUTHOR]

This article discusses methods of parametric and structural-parametric synthesis of resonators, which contain transmission line segments of the same length. These methods allow optimizing resonators of various configurations with different segments number $N$ and characteristic impedances $Z_{0i}$. We will call optimal resonators if they have a minimum ratio $m = Z_{\mathrm {0max}}/Z_{\mathrm {0min}}$ at given ratio between the critical frequencies (poles and zeros of the input impedance). The input impedances of the resonators under consideration at the Richards’ frequency variable $S$ are described by the reactance functions of $N$ degree: $Z^{N}(S)$. The parametric synthesis refers to stepped impedance resonators (SIRs). The structural-parametric synthesis refers to resonators of a more general form, which contain open- and short-circuited stubs. The methods are based on two states established in this paper: the order of the reactance function $Z^{N}(S)$ should be the minimum possible ($N = N_{\mathrm {min}}$) to implement the required ratio between certain critical frequencies; the characteristic impedances of the composite sections $Z_{0i}$ take only two values $Z_{\mathrm {0min}}$ or $Z_{\mathrm {0max}}$. These methods were used to synthesize different resonators useful for practical application. With the help of the structural-parametric synthesis, the resonators of a more complex configuration are synthesized, which significantly exceed SIRs. The results of the measurements and simulation are presented. [ABSTRACT FROM AUTHOR]

In this paper, the first dual-band sub-sampling receiver front end with sampling frequency optimization to meet the ultimate receiver error vector magnitude (EVM) of −40 dB over wide input power range of 19 dB is proposed. A systematic sub-sampling receiver chain EVM optimization with respect to major system-level impairments, such as noise folding, sampling frequency, IQ mismatches, phase noise of the sub-sampling clock, and unit capacitor value realizable at the decimation filter, is presented. The proposed dual-band sub-sampling receiver has a 26–41 dB continuously tunable gain for 2.4 GHz and 26–38.5 dB for the 5 GHz WLAN band. Continuously tunable gain ensures the ultimate receiver EVM performance over wider input power levels. In addition, the 5 GHz band is continuously tunable from 4.5 to 5.7 GHz. An active balun feedback low-noise amplifier followed by a sub-sampling down-conversion mixer is implemented to down-convert both WLAN bands to an intermediate frequency in the range from 445 to 538 MHz. Sub-sampling frequency optimization proposed in this paper down-converts both WLAN bands with the sampling frequency from 1.78 to 2.15 GHz to reach the target EVM. Additionally, a switched capacitor decimation filter running at 90 MHz is implemented to provide dual functionalities of down-conversion to baseband and band selection. A test-chip is implemented in a 1.2 V 65-nm CMOS technology. The proposed dual-band sub-sampling receiver occupies a total active area of 0.72 mm2 and has a total power dissipation of 55.6 mW. The overall receiver chain shows a noise figure of 11.5 dB at the highest gain and an IIP3 of −8 dBm at the lowest gain. [ABSTRACT FROM AUTHOR]

Advanced power amplifier (PA) architectures are critical for 5G communication which requires PAs to have sufficient output power, high linearity, and high energy efficiency. The time-variant operational environment further demands the self-reconfigurability in the PA design. Recent developments in bandit-problems and reinforcement learning (RL) have led to data-driven control algorithms. This paper presents various RL-based algorithms for Doherty PA control, which achieve robust adaptive operation over environmental changes. Multiple RL frameworks are incorporated in the control algorithms including multi-armed bandit (MAB), continuum-armed bandit (CAB), contextual-bandit (CB), and actor-critic with experience replay (AC). The control algorithms based on the latter three frameworks leverage on the prior information about the Doherty PA’s characteristics to improve learning efficiency. In our simulation test where the optimal policy needs to be adjusted due to transmitter output load impedance mismatch, the MAB-based control learns the optimal policy within 25,000 samples. The control algorithms based on CAB and CB learn the optimal policy within 5,000 samples. The fastest learning rate is achieved by the control algorithm based on AC, which learns the optimal policy within 1,500 samples. [ABSTRACT FROM AUTHOR]

Conventional design and optimization of passive compensation network (PCN) for inductive power transfer (IPT) system are based on specific topologies. The demerits of this design method are: i) The topology is mostly chosen by experience; ii) The design parameters are not multi-objective optimal. Aiming at these issues, this paper proposes an optimal PCN design scheme based on evolutionary algorithm (EA) to synchronously optimize the topology and parameters of PCN for IPT system. Firstly, a unified mathematical model of the PCN is presented and derived by transmission matrix. Then, according to the mathematical model, the multi-objective functions (such as output fluctuation and efficiency) as well as the constraints (such as load and coupling coefficient) for the optimal PCN design are established. The EA based multi-objective optimal PCN design algorithm is further constructed. Six optimal results are obtained using the algorithm, and one optimized PCN having minimum output current fluctuation and high-efficiency is chosen to validate the effectiveness of the proposed design scheme in experiment. For the given IPT system with the optimized PCN, the maximum fluctuation of output current is no more than 11%, within 200% of load variation and about 77% of coupling variation. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) has been widely studied due to its high performance and usability for various applications such as image classification, detection, segmentation, translation, and action recognition. Thanks to the universal applications and high performance of DNN algorithm, DNN is adopted for various AI platforms, including edge/mobile devices as well as cloud servers. However, high-performance DNN requires a large amount of computation and memory access, making it challenging to implement DNN operation on edge/mobile. There have been several ways to solve these problems, including algorithms as well as hardware for DNN. Algorithms that help accelerate DNN in hardware enable much more efficient operation of high-performance AI. This article aims to provide an overview of the recent hardware and algorithm co-design schemes enabling efficient processing of DNNs. Specifically, it will provide algorithm optimization methods for DNN structure, neurons, synapses, and data types. This paper also introduces optimization methods for hardware architectures, PE array, data-path control, and microarchitecture of PE. And we will also show examples of DNN algorithm and hardware co-designed ASICs. [ABSTRACT FROM AUTHOR]

This paper presents an ultra-broadband 5-bit switched-type attenuator (STA). Three attenuation topologies are employed for the design of the attenuation cells, including the T-type, simplified T-type and Π-type topologies. For each attenuation topology, the optimal values of the series and shunt resistors are derived to achieve accurate amplitude tuning and ensure good impedance matching. The capacitive compensation technique is adopted by both the T-type and Π-type topologies to enhance the high-frequency performance. Based on the proposed optimization techniques, a systematic design methodology is developed for the STA. The 5-bit STA is implemented in 65-nm CMOS technology and occupies a core chip area of only 0.036 mm2. It exhibits ultra-broadband operation with 15.5-dB amplitude tuning range and 0.5-dB tuning step. The insertion loss of the reference state is 1.5 – 5.9 dB from DC to 50 GHz. The return loss is better than 12 dB for all the 32 states. The RMS amplitude error and phase error are less than 0.25 dB and 3.5° over the DC – 50 GHz band. [ABSTRACT FROM AUTHOR]

Quantization is a promising technique to compress the size of Convolutional Neural Network (CNN) models. Recently, various precision-scalable designs have been presented to reduce the computational complexity in CNNs. However, most of them adopt straightforward calculation scheme to implement the CNN, which causes high bandwidth requirement and low hardware utilization efficiency. This paper proposes a new precision-scalable architecture which can fully reduce the computational complexity in CNN inference and meanwhile has a finely simplified calculation scheme. Based on the proposed scheme, a well-optimized multiplier called Compositional Processing Element (C-PE) is devised. Compared with the previous multipliers, the new C-PE requires less area and power. Furthermore, two levels of optimization are introduced to the design to relieve the bandwidth problem and increase the hardware utilization efficiency. Implemented under the TSMC 90nm CMOS technology, the whole design achieves 6-68.1 fps in various precisions on VGG16 benchmark and a 49.8TOPS/W energy efficiency at 500MHz when scaled to 28nm, which is much better than previous precision-scalable ones. [ABSTRACT FROM AUTHOR]

This paper addresses a general method for parameter identification in distributed parameter systems (DPSs) based on a reference model and measurement information. Moreover, the developed identification method is used to deal with secure communication problems. Firstly, in combination with the historical information measured by sensors, an iterative approach based on an approximator is put forward. Meanwhile, an execution scheme for the iterative approach is provided by an optimization policy. Subsequently, by bringing new mechanisms into quantum particle swarm optimization (QPSO) algorithm to enhance its search capability, we propose quantum-leading-following-based optimization (QLFBO) algorithm, which serves the optimization policy. Further, the global convergence for QLFBO algorithm is proved combined with the parameter identification problem of DPSs, and the computational complexity on this algorithm is discussed in detail. Finally, simulation experiments on the diffusion-reaction model are carried out by the iterative approach based on QLFBO algorithm under different measurement ways. Simultaneously, secure communication depending on this model is performed in the double-scroll Chua’s circuit and a lotus image via the investigated iterative strategy and encryption-decryption scheme. Simulation results verify the validity and practicability of the developed method. [ABSTRACT FROM AUTHOR]

This is a companion paper of [Mixed equilibrium solution of time-inconsistent stochastic linear-quadratic problem, SIAM J. Control Optim., vol. 57, no. 1, 533–569, 2019], where general theory has been established to characterize the open-loop equilibrium control, feedback equilibrium strategy and mixed equilibrium solution for a time-inconsistent stochastic linear-quadratic problem. This note is, on the one hand, to test the developed theory of that paper and on the other hand to push the solvability of multiperiod mean-variance portfolio selection. A nondegenerate assumption, which is popular in the existing literature about multiperiod mean-variance portfolio selection, has been removed in this note; and neat conditions have been obtained to characterize the existence of equilibrium solutions. [ABSTRACT FROM AUTHOR]

This paper considers the quadratic control problem of discrete-time Markov jump linear systems with constraints on the norm of the state and control variables. We assume that the Markov chain parameter is not available, and instead, there is a detector, which emits signals providing information on this parameter. It is desired to derive a feedback linear control using the information provided by this detector in order to stochastically stabilize the closed-loop system, satisfy the constraints whenever the initial conditions belong to an invariant set, and minimize an upper bound for the quadratic cost. We show that a linear matrix inequality (LMI) optimization problem can be formulated in order to obtain a solution for this problem. Two other related problems, one for minimizing the guaranteed quadratic cost considering fixed initial conditions and the other for maximizing an estimate of the domain of an invariant set, can also be formulated using our LMI approach. This paper is concluded with some numerical simulations. [ABSTRACT FROM AUTHOR]

This paper proposes scalable, distributed algorithms for solving linear equations by integrating two mechanisms, termed consensus and conservation, in double-layered multiagent networks. The multiagent network considered in this paper is composed of clusters and each cluster consists of an aggregator and a subnetwork of agents. By achieving consensus and conservation through agent–agent communications in the same cluster and aggregator–aggregator communications among different clusters, respectively, distributed algorithms are devised for agents to cooperatively achieve a solution to the overall linear equation. These algorithms outperform existing algorithms, including but not limited to the following aspects—first, each agent does not have to know as much as a complete row or column of the overall equation; second, each agent only needs to control as few as two scalar states when the number of clusters and the number of agents are sufficiently large; third, the dimensions of agents’ states in the proposed algorithms do not have to be the same (while in contrast, algorithms based on the idea of standard consensus inherently require all agents’ states to be of the same dimension). Both analytical proof and simulation results are provided to validate exponential convergence of the proposed distributed algorithms in solving linear equations. [ABSTRACT FROM AUTHOR]

This paper studies multi-stage capacitive mixed-signal matrix-vector multiplying digital-to-analog (MDAC) conversion topologies for highly energy-efficient, high-resolution, and high-dimensional MIMO analog processing systems. In order to mitigate nonlinearity due to radix errors and capacitive mismatch encountered in compact low-power MDAC realizations, we introduce stochastic successive approximation, or S2A, as an online optimization algorithm for adaptive array analog signal processing amenable to efficient implementation in massively parallel mixed-signal hardware. S2A offers a direct alternative to stochastic gradient descent overcoming several of its shortcomings, such as its sensitivity to model error, while improving on the rate and quality of convergence. S2A overcomes non-convergence typically encountered with gradient descent for non-convex optimization landscapes induced by a mismatch in capacitive multiplying digital-to-analog converter components when applied to adaptive analog signal processing. Experimental validation of S2A in mixed-signal hardware for real-time RF adaptive beamforming demonstrates 65 dB of over-the-air, multipath interferer suppression in fewer than 25 S2A iterations. [ABSTRACT FROM AUTHOR]

Design of hardware efficient digital systems has drawn appreciable attention of researchers over the last few decades. To this aim, this paper considers the design of a narrow transition band finite impulse response (FIR) filter operated in an interpolated bandpass filtering approach. An optimization problem is formulated that minimizes the complexity measure of the narrow transition band FIR filter under the constraints of length of the filter and normalized peak ripple magnitude (NPRM) threshold. Closed-form expressions for the optimal interpolation factor as well as the passband and stopband edges to obtain the narrow transition band are derived. A designed filter is subsequently implemented on Altera’s Cyclone IV field programmable gate array (FPGA) chip and supremacy of the suggested design has strongly been established by correlating its hardware cost with many of the state-of-the-art FIR filters. [ABSTRACT FROM AUTHOR]

Recently, symmetric block-wise concatenated-BCH (SBC-BCH) codes are proposed as strong error-correcting codes (ECCs) based on hard-decision channel outputs, which is especially suited for storages using NAND flash memories. Targeting energy-efficient NAND flash memory applications, this paper presents an energy-optimized decoder architecture which includes an iterative decoder for a SBC-BCH code as a main decoder and a low-complexity auxiliary decoder for a block-wise single parity-check (BSPC) code. The auxiliary decoder is opportunistically in action to break the dominant error bound associated with the SBC-BCH code, which allows one to lower the uncorrectable bit-error-rate (UBER) to 10−15 in an energy efficient way. This work presents several design-level optimizations for further enhancing the energy-efficiency of the iterative SBC-BCH decoder. More precisely, the new initialization scheme is proposed for ensuring the energy-efficient seamless decoding scenario. The syndrome tracking is applied to eliminate the previous syndrome calculation and the reordered Chien search further enhances the energy-efficiency as well as the decoding throughput. Targeting a 0.9-rate 4KB SBC-BCH code for commercialized storages using NAND flash memories, a prototype decoder consisting of both the iterative main and auxiliary decoders is designed in a 65-nm CMOS process. By applying the proposed optimizations, the prototype decoder achieves an energy-efficiency of 3.43 pJ/b while providing a decoding throughput of 13.2 Gb/s, which is superior to the previous state-of-the-art decoders for mobile storages. [ABSTRACT FROM AUTHOR]

The paper considers normalized gradient descent (NGD), a natural modification of classical gradient descent (GD) in optimization problems. It is shown that, contrary to GD, NGD escapes saddle points “quickly.” A serious shortcoming of GD in nonconvex problems is that it can take arbitrarily long to escape from the neighborhood of a saddle point. In practice, this issue can significantly slow the convergence of GD, particularly in high-dimensional nonconvex problems. The paper focuses on continuous-time dynamics. It is shown that 1) NGD “almost never” converges to saddle points and 2) the time required for NGD to escape from a ball of radius $r$ about a saddle point $x^*$ is at most $5\sqrt{\kappa }r$ , where $\kappa$ is the condition number of the Hessian of $f$ at $x^*$. As a simple application of these results, a global convergence-time bound is established for NGD under mild assumptions. [ABSTRACT FROM AUTHOR]

Recently, Thompson sampling has been shown to achieve good theoretical performance guarantees for stochastic control problems with parameter uncertainty when the state, control, and parameter spaces are all finite. Much less is known however about the performance of Thompson sampling when applied to continuous or more general spaces, which constitutes an important class of problems in practice. In this paper, we study Thompson sampling when applied to a broad class of average cost stochastic control problems where the state, control, and parameter spaces are all general measurable spaces. The main contributions of our paper are establishing theoretical performance guarantees for Thompson sampling as measured by: first, expected posterior sampling error; and second, average per period regret. [ABSTRACT FROM AUTHOR]

In this paper, we address the so-called Liouville control problem for discrete-time SISO linear systems in the class of Gaussian distributions. In particular, we propose a systematic procedure for the characterization of a dynamic output feedback policy that will transfer the output of the system, which is a known Gaussian random variable, to a goal Gaussian distribution after a finite number of stages. In the proposed approach, the Liouville control problem is reduced to two decoupled (finite-dimensional) quadratic programs, one of which is subject to a single affine constraint, which is a convex program, whereas the other one is subject to a single quadratic equality constraint. Despite the fact that the second optimization problem is not convex, one can characterize its exact solution via a systematic procedure without resorting to convex relaxation techniques, which may yield suboptimal or even infeasible solutions to the original (nonconvex) optimization problem. Finally, we present numerical simulations that illustrate the key ideas of this paper. [ABSTRACT FROM AUTHOR]

This paper deals with the estimation of convergence rate and domain of attraction of control-Lyapunov functions in Lyapunov-based control. This pair of estimation problems has been considered only for input-affine systems with constraints on the input norm. In this paper, we propose a novel optimization framework to address the estimation of convergence rate and domain of attraction. Specifically, we formulate the estimation problems as min–max bilevel programs for the decay rate of the Lyapunov function, where the inner problem can be resolved using Karush–Kuhn–Tucker optimality conditions, and the resulting single-level programs can be transformed into and solved as mixed-integer nonlinear programs. The proposed approach is applicable to systems with input-nonaffinity or more general forms of input constraints under an input-convexity assumption. [ABSTRACT FROM AUTHOR]

A fundamental challenge in multiagent systems is to design local control algorithms to ensure a desirable collective behavior. The information available to the agents, gathered either through communication or sensing, naturally restricts the achievable performance. Hence, it is fundamental to identify what piece of information is valuable and can be exploited to design control laws with enhanced performance guarantees. This paper studies the case when such information is uncertain or inaccessible for a class of submodular resource allocation problems termed covering problems. In the first part of this paper, we pinpoint a fundamental risk-reward tradeoff faced by the system operator when conditioning the control design on a valuable but uncertain piece of information, which we refer to as the cardinality, that represents the maximum number of agents that can simultaneously select any given resource. Building on this analysis, we propose a distributed algorithm that allows agents to learn the cardinality while adjusting their behavior over time. This algorithm is proved to perform on par or better to the optimal design obtained when the exact cardinality is known a priori. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the fuzzy constrained matrix game (MG) problems using the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first in attempting to find a solution for fuzzy game problems using RNN models. For this purpose, a fuzzy game problem is reformulated into a weighting problem. Then, the Karush–Kuhn–Tucker (KKT) optimality conditions are provided for the weighting problem. The KKT conditions are used to propose the RNN model. Moreover, the Lyapunov stability and the global convergence of the RNN model are also confirmed. Finally, three illustrative examples are presented to demonstrate the effectiveness of this approach. The obtained results are compared with the results obtained by the previous approaches for solving fuzzy constrained MG. [ABSTRACT FROM AUTHOR]

Unsupervised feature selection is fundamentally important for processing unlabeled high-dimensional data, and several methods have been proposed on this topic. Most existing embedded unsupervised methods just emphasize the data structure in the input space, which may contain large noise. Therefore, they are limited to perceive the discriminative information implied within the low-dimensional manifold. In addition, these methods always involve several parameters to be tuned, which is time-consuming. In this paper, we present a self-tuned discrimination-aware (STDA) approach for unsupervised feature selection. The main contributions of this paper are threefold: 1) it adopts the advantage of discriminant analysis technique to select the valuable features; 2) it learns the local data structure adaptively in the discriminative subspace to alleviate the effect of data noise; and 3) it performs feature selection and clustering simultaneously with an efficient optimization strategy, and saves the additional efforts to tune parameters. Experimental results on a toy data set and various real-world benchmarks justify the effectiveness of STDA on both feature selection and data clustering, and demonstrate its promising performance against the state of the arts. [ABSTRACT FROM AUTHOR]

The computation-intensive circuit simulation makes the analog circuit sizing challenging for large-scale/complicated analog/RF circuits. A Bayesian optimization approach has been proposed recently for the optimization problems involving the evaluations of black-box functions with high computational cost in either objective functions or constraints. In this paper, we propose a weighted expected improvement-based Bayesian optimization approach for automated analog circuit sizing. Gaussian processes (GP) are used as the online surrogate models for circuit performances. Expected improvement is selected as the acquisition function to balance the exploration and exploitation during the optimization procedure. The expected improvement is weighted by the probability of satisfying the constraints. In this paper, we propose a complete Bayesian optimization framework for the optimization of analog circuits with constraints for the first time. The existing GP model-based optimization methods for analog circuits take the GP models as either offline models or as assistance for the evolutionary algorithms. We also extend the Bayesian optimization algorithm to handle multi-objective optimization problems. Compared with the state-of-the-art approaches listed in this paper, the proposed Bayesian optimization method achieves better optimization results with significantly less number of simulations. [ABSTRACT FROM PUBLISHER]

Analytical noise formulations and design optimization of single- and dual-band inductively source-degenerated MOS low-noise amplifiers (LNAs) with the substrate and metal loss effects of on-chip inductors are established in this paper. It reveals that the noise figures of both single-/dual-band LNAs degrade substantially under the consideration of the loss effects. However, by increasing the device size, the noise optimization methodology of simultaneous noise and input match for a single-band LNA still holds true with concern for the loss effects. For a dual-band LNA, analytical noise optimization for a balanced noise design is established for inductors with metal and substrate loss and indicates a larger device size. The substrate and metal loss effects of inductors can be mitigated using integrated passive device (IPD) process for the input match network. The demonstrated single-band 0.18- $\mu \text{m}$ MOS LNAs with and without IPD process show noise figures of 1.53 and 2.52 dB at 2.4 GHz, respectively. Subsequently, the implemented dual-band 0.18- $\mu \text{m}$ MOS LNAs with and without IPD process show noise figures of 1.6/2.6 and 3.25/4.1 dB at 2.4/5 GHz, respectively. [ABSTRACT FROM AUTHOR]

The prevalence of wireless networks has made the long-term need for communications security more imperative. In various wireless applications, images and/or video constitute critical data for transmission. For their copyright protection and authentication, watermarking can be used. In many cases, the cost of wireless nodes must be kept low, which means that their processing and/or power capabilities are very limited. In such cases, low-cost hardware implementations of digital image/video watermarking techniques are necessary. However, to end up with such implementations, proper selection of watermarking techniques is not enough. For this reason, in this paper, we introduce computation optimizations of the implemented algorithm to keep the integer part of arithmetic operations at optimal size, and, hence, arithmetic units as small as possible. In addition, further analysis is performed to reduce quantization error. Three different hardware-architecture variants, two for image watermarking and one for video (pipelined), are proposed, which reutilize the already small arithmetic units in different computation steps, to further reduce implementation cost. The proposed designs compare favorably to already existing implementations in terms of area, power, and performance. Moreover, the watermarked images’/frames’ errors, compared to their floating point counterparts, are very small, while robustness to various attacks is high. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many filters to extract the knowledge behind it. However, while the depth of convolutional layers gets deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, inspired by rate-distortion optimization in image and video coding, we propose a computation-performance optimization (CPO) method to remove the redundant convolution filters in a CNN with performance constraints. To prove the effectiveness of the proposed method, CPO is applied to the networks for image super-resolution and image classification. Under almost the same PSNR drop and accuracy drop for performance evaluation in these two tasks, we can achieve the best parameter and computation reduction when compared with previous works. [ABSTRACT FROM AUTHOR]

This paper considers the optimization problem for partially observable Markov decision processes (POMDPs) with the continuous state, observation, and action spaces. POMDPs with the discrete spaces have emerged as a promising approach to the decision systems with imperfect state information. However, in recent applications of POMDPs, there are many problems that have continuous states, observations, and actions. For such problems, due to the infinite dimensionality of the belief space, the existing studies usually discretize the continuous spaces with the sufficient or nonsufficient statistics, which may cause the curse of dimensionality and performance degradation. In this paper, based on the sensitivity analysis of the performance criteria, we have developed a simulation-based policy iteration algorithm to find the local optimal observation-based policy for POMDPs with the continuous spaces. The proposed algorithm needs none of the specific assumptions and prior information, and has a low computational complexity. One numerical example of the complicated multiple-input multiple-output beamforming problem shows that the algorithm has a significant performance improvement. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed computation of the well-known linear matrix equation in the form of ${{AXB}} = F$ , with the matrices $A$ , $B$ , $X$ , and $F$ of appropriate dimensions, over multiagent networks from an optimization perspective. In this paper, we consider the standard distributed matrix-information structures, where each agent of the considered multiagent network has access to one of the subblock matrices of $A$ , $B$ , and $F$. To be specific, we first propose different decomposition methods to reformulate the matrix equations in standard structures as distributed constrained optimization problems by introducing substitutional variables; we show that the solutions of the reformulated distributed optimization problems are equivalent to least squares solutions to original matrix equations; and we design distributed continuous-time algorithms for the constrained optimization problems, even by using augmented matrices and a derivative feedback technique. Moreover, we prove the exponential convergence of the algorithms to a least squares solution to the matrix equation for any initial condition. [ABSTRACT FROM AUTHOR]

We study in this paper, a class of constrained linear-quadratic (LQ) optimal control problem formulations for the scalar-state stochastic system with multiplicative noise, which has various applications, especially in the financial risk management. The linear constraint on both the control and state variables considered in our model destroys the elegant structure of the conventional LQ formulation and has blocked the derivation of an explicit control policy so far in the literature. We successfully derive in this paper, the analytical control policy for such a class of problems by utilizing the state separation property induced from its structure. We reveal that the optimal control policy is a piecewise affine function of the state and can be computed offline efficiently by solving two coupled Riccati equations. Under some mild conditions, we also obtain the stationary control policy for an infinite time horizon. We demonstrate the implementation of our method via some illustrative examples and show how to calibrate our model to solve dynamic constrained portfolio optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, an exchange algorithm is proposed to design sparse linear phase finite impulse response (FIR) filters with reduced effective length. The sparse FIR filter design problem is formally an ${l} _{0}$ -norm minimization problem. This original design problem is re-formulated by encoding the filter coefficients using a binary encoding vector, which represents the locations of the zero and non-zero filter coefficients. An iterative 0-1 exchange process with proper direction control is proposed to propel the minimax approximation error toward the specified upper bound of error for sparsity maximization. The effective length is optimized with a lower priority than sparsity in the proposed algorithm. Simulation results show that the proposed algorithm is superior to the existing algorithms in terms of both sparsity and/or effective length in most cases. [ABSTRACT FROM AUTHOR]

Traffic congestion often incurs environmental problems. One of the most effective ways to mitigate this is carpooling transportation, which substantially reduces automobile demands. Due to the popularization of smartphones and mobile applications, a carpool service can be conveniently accessed via the intelligent carpool system. In this system, the service optimization required to intelligently and adaptively distribute the carpool participant resources is called the carpool service problem (CSP). Several previous studies have examined viable and preliminary solutions to the CSP by using exact and metaheuristic optimization approaches. For CSP-solving, evolutionary computation (e.g., metaheuristics) is a more promising option in comparison to exact-type approaches. However, all the previous state-of-the-art approaches use pure optimization to solve the CSP. In this paper, we employ the framework of neuroevolution to propose the self-organizing map-based neuroevolution (SOMNE) solver by which the SOM-like network represents the abstract CSP solution and is well-trained by using neural learning and evolutionary mechanism. The experimental section of this paper investigates the comparisons and analyses of two objective functions of the CSP and demonstrates that the proposed SOMNE solver achieves superior results when compared against those the other approaches produce, especially in regard to the optimization of the primary objective functions of the CSP. Finally, the visual results of the SOM are illustrated to show the effectiveness and efficiency of the evolutionary neural learning process. [ABSTRACT FROM AUTHOR]

In this paper, different methodologies are employed to improve the linearity performance of a prototype CMOS image sensor (CIS). First, several pixel structures, including a novel pixel design based on a capacitive trans-impedance amplifier (CTIA), are proposed to achieve a higher pixel-level linearity. Furthermore, three types of digital linearity calibration methods are explored. A prototype image sensor designed in 0.18- $\mu \text{m}$ , 1-poly, and 4-metal CIS technology with a pixel array of $128 \times 160$ is used to verify these linearity improvement techniques. The measurement results show that the proposed CTIA pixel has the best linearity result out of all pixel structures. Meanwhile, the proposed calibration methods further improved the linearity of the CIS without changing the pixel structure. The pixel mode method achieves the most significant improvement on the linearity. One type of 4T pixel attains a nonlinearity of 0.028% with pixel mode calibration, which is two times better than the state of the art. Voltage mode (VM) and current mode (CM) calibration methods get rid of the limitation on the illumination condition during calibration operation; especially, CM calibration can further suppress the nonlinearity caused by the integration capacitor $C_{\textit {FD}}$ on the floating diffusion node, which is remnant in VM. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new classification algorithm called the optimization of distribution differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as possible to one another, whereas the gravity centers of these classes are as far as possible from one another. This aim is formulated as a multiobjective optimization problem that is solved by a hybrid of an evolutionary strategy and the quasi-Newton method. The choice of the transformation function is flexible and could be any continuous space function. We experiment with a linear and a nonlinear transformation in this paper. We show that the algorithm can outperform eight other classification methods, namely naive Bayes, support vector machines, linear discriminant analysis, multilayer perceptrons, decision trees, and $k$ -nearest neighbors, and two recently proposed classification methods, in 12 standard classification data sets. Our results show that the method is less sensitive to the imbalanced number of instances compared with these methods. We also show that ODD maintains its performance better than other classification methods in these data sets and hence offers a better generalization ability. [ABSTRACT FROM AUTHOR]

In this paper, we present a framework for analytically estimating the output quality of common digital signal processing (DSP) blocks that utilize approximate adders. The framework is based on considering the error of approximate adders as an additive noise (approximation noise) that disturbs the output of the DSP block in question. A signal processing theoretical modeling approach for describing the power of the approximation noise which is the integral of error spectral density over the bandwidth, is developed. The output qualities of DSP blocks, such as finite impulse response filter, discrete cosine transform, and fast Fourier transform, which utilize approximate adders, are thus estimated. The accuracy of the proposed framework is evaluated by comparing mathematical model predictions to simulation results by using the signal-to-noise ratio (SNR) metric. The inaccuracy of the SNRs predicted by the framework was, on average, less than 2.5dB compared with that obtained from simulations. Therefore, a mathematical optimization approach based on Lagrange Multipliers for optimizing design parameters is also presented. The optimization is realized by choosing a proper configuration of the target block, such as determining the data width of the inexact computation part for each approximate adder in the design. [ABSTRACT FROM AUTHOR]

This paper is concerned with the optimal static distributed control problem for linear discrete-time deterministic and stochastic systems. The objective is to design a stabilizing static distributed controller whose performance is close to that of the optimal centralized controller. To this end, we first consider deterministic systems, where the initial state is either given or belongs to a known bounded region. Given an arbitrary centralized controller, we derive a condition under which there exists a distributed controller that generates input and state trajectories close to their counterparts in the centralized closed-loop system. This condition for the design of a distributed controller is translated into an optimization problem, where the optimal objective value of this problem quantifies the closeness of the designed distributed and given centralized control systems. The results are then extended to stochastic systems that are subject to input disturbance and measurement noise. The proposed optimization problem has a closed-form solution (explicit formula) and can be efficiently solved for large-scale systems. The mathematical framework developed in this paper is utilized to design a near-globally optimal distributed controller based on the optimal centralized controller, and strong theoretical lower bounds on the global optimality guarantee of the obtained distributed controller are derived. We show that if the optimal objective value of the proposed convex program is sufficiently small, the designed controller is stabilizing and nearly globally optimal. To illustrate the results, case studies on aircraft formation and frequency control of power systems are offered. [ABSTRACT FROM AUTHOR]