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This paper presents a novel hardware architecture for adaptive systems whose exact specification is unknown. The architecture is suitable for linear and nonlinear systems whose inputs are real or complex signals (variables), and that have an acceptable representation as low order polynomials in these variables. The implementation is based on using an a priori selected subset of Walsh spectral coefficients. The proposed architecture can acquire its target functionality and adapt itself to changing environments even if the number of variables, their order and precision are unknown in advance. This is in contrast to conventional multiply-and-accumulate (MAC) based architectures where this information must be determined before the design and implementation of the system. In this context (of systems whose functionality is unknown), the delay and the implementation cost of the proposed architecture is significantly lower than MAC-based solutions. [ABSTRACT FROM AUTHOR]

As one of the efficient approaches to deal with big data, divide-and-conquer distributed algorithms, such as the distributed kernel regression, bootstrap, structured perception training algorithms, and so on, are proposed and broadly used in learning systems. Some learning theories have been built to analyze the feasibility, approximation, and convergence bounds of these distributed learning algorithms. However, less work has been studied on the stability of these distributed learning algorithms. In this paper, we discuss the generalization bounds of distributed learning algorithms from the view of algorithmic stability. First, we introduce a definition of uniform distributed stability for distributed algorithms and study the distributed algorithms’ generalization risk bounds. Then, we analyze the stability properties and generalization risk bounds of a kind of regularization-based distributed algorithms. Two generalization distributed risks obtained show that the generalization distributed risk bounds for the difference between their generalization distributed and empirical distributed/leave-one-computer-out risks are closely related to the size of samples $n$ and the amount of working computers $m$ as $\mathcal {O}(m/{n}^{1/2})$. Furthermore, the results in this paper indicate that, for a good generalization regularized distributed kernel algorithm, the regularization parameter $\lambda $ should be adjusted with the change of the term $m/{n}^{1/2}$. These theoretic discoveries provide the useful guidance when deploying the distributed algorithms on practical big data platforms. We explore our theoretic analyses through two simulation experiments. Finally, we discuss some problems about the sufficient amount of working computers, nonequivalence, and generalization for distributed learning. We show that the rules for the computation on one single computer may not always hold for distributed learning. [ABSTRACT FROM AUTHOR]

Large scale quantum computers will break classical public-key cryptography protocols by quantum algorithms such as Shor’s algorithm. Hence, designing quantum-safe cryptosystems to replace current classical algorithms is crucial. Luckily there are some post-quantum candidates that are assumed to be resistant against future attacks from quantum computers, and NIST is considering standardizing them. Among these candidates, lattice-based cryptography sounds more interesting than others due to the performance results as well as confidence in the security. There are few works in the literature evaluating the performance of lattice-based cryptography in hardware. In this paper, we focus on Cryptographic Suite for Algebraic Lattices (CRYSTALS) key exchange mechanisms known as Kyber and provide an instruction-set hardware architecture and implement on Xilinx Artix-7 FPGA for performance evaluation and testing. Our proposed architecture provides an efficient and high-performance set of components to perform polynomial sampling, number-theoretic transform (NTT), and point-wise multiplication to speed up lattice-based post-quantum cryptography (PQC). This architecture implemented on ASIC outperforms state-of-the-art implementations. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal control of service rates of a tandem queue with power constraints. The service rate of a server is determined by the power allocated to that server. The total power of the system is fixed. The system cost is comprised of two parts, the holding cost reflecting the congestion of queues and the operating cost reflecting the power consumed at servers. The optimization objective is to find the optimal power allocation policy among servers, which can minimize the system average cost. We formulate this problem as a Markov decision process with a constrained action space. Sensitivity-based optimization theory is applied to study this problem. The necessary and sufficient condition of optimal service rates, and the optimality of the vertexes of the feasible domain are derived when the operating cost has a linear or concave form. An iterative algorithm is further developed to find the optimal service rates. This algorithm may work well even when the cost function has a general form. The extension to general tandem queues with many servers is also studied. Finally, we conduct numerical experiments under different parameter settings to demonstrate the main idea of this paper. [ABSTRACT FROM AUTHOR]

With the advent of technology over the recent years, the low-density parity-check (LDPC) codes, which were once seen as an impractical concept, are now poised to be the next big thing in the communication standards of today for their near-capacity performances. Nonetheless, the physical implementation of LDPC decoders is more often than not encumbered by the arithmetic of its decoding algorithm. Entangled by numerous computations of minima, LDPC decoders not only require considerable amount of resources to the implement cascaded pair-wise comparators, but also yield low decoding throughputs. In this paper, we propound a novel design for the computation of minimum and subminimum in LDPC decoding, known as the cognitive-amplification detector (CAD). By leveraging on the finite precision of fixed-point binary representation in actual hardware, our CAD proposition renders significant gains in decoding throughput and savings in resource consumption of up to 20% and 15%, respectively, not to mention negligible trade-offs in error-correcting capabilities. [ABSTRACT FROM AUTHOR]

This paper presents a full hardware implementation of the eXtended Merkle Signature Scheme (XMSS), a NIST approved and IETF RFC specified post-quantum cryptography (PQC) algorithm. An optimized node traversal is proposed to enable efficient memory utilization without compromising the computational latency of the L-tree and Merkle tree construction, which are two key components used for the compression of the Winternitz One-Time Signature (WOTS) public key in XMSS. The computation of the authentication path during signature generation has also been significantly sped up by our proposed hardware implementation of the Buchmann, Dahmen, and Schneider (BDS) algorithm. Our implementation has completely avoided the use of block random-access memory, which is known to be vulnerable to side-channel attacks. The memory requirement has been highly optimized for implementation with small flip-flop chains and register counters as pointers for fast data access. To the best of our knowledge, this is the first full hardware implementation of all three key generation, signing and verification operations of XMSS. The design has been prototyped and evaluated on a 28 nm FPGA platform to demonstrate its performance improvements over the most efficient software and hardware/software co-design methods reported to date. Specifically, it increases the computational efficiency of the best reported XMSS implementation for key generation and signature generation by about 20% and 50%, respectively. It can also run at 10% higher clock speed than the fastest hardware implementation of signature verification in FPGA with 8% lower hardware resource utilization. [ABSTRACT FROM AUTHOR]

A design methodology for incorporating Residue Number System (RNS) and Polynomial Residue Number System (PRNS) in Montgomery modular multiplication in GF(p) or GF(2^n) respectively, as well as a VLSI architecture of a dual-field residue arithmetic Montgomery multiplier are presented in this paper. An analysis of input/output conversions to/from residue representation, along with the proposed residue Montgomery multiplication algorithm, reveals common multiply-accumulate data paths both between the converters and between the two residue representations. A versatile architecture is derived that supports all operations of Montgomery multiplication in GF(p) and GF(2^n), input/output conversions, Mixed Radix Conversion (MRC) for integers and polynomials, dual-field modular exponentiation and inversion in the same hardware. Detailed comparisons with state-of-the-art implementations prove the potential of residue arithmetic exploitation in dual-field modular multiplication. [ABSTRACT FROM PUBLISHER]

This paper presents a practical method for designing fixed-point FIR filters. The proposed method takes both the filter's magnitude response and its hardware cost into consideration in the design process. The method constructs a basis set based on the fixed-point coefficients that have been synthesized already. The elements in the basis set are used to synthesize the undetermined fixed-point coefficients later. Thus, this basis set expands gradually along with the progress of the coefficient design. The method employs some strategies to speed up the design process. For example, a complexity estimation strategy helps us stop digging deeper in some branches of the search tree, and a solution prediction strategy for high-order FIR filters helps us design fixed-point FIR filters of length equal to a few hundreds. Applying the proposed method to design twenty benchmark cases, we can obtain hardware-efficient results in a reasonable design time. In two long filter design cases, our design results are better than those designed by the other methods. [ABSTRACT FROM AUTHOR]

Universal memcomputing machines (UMMs) represent a novel computational model in which memory (time nonlocality) accomplishes both tasks of storing and processing of information. UMMs have been shown to be Turing-complete, namely, they can simulate any Turing machine. In this paper, we first introduce a novel set theory approach to compare different computational models and use it to recover the previous results on Turing-completeness of UMMs. We then relate UMMs directly to liquid-state machines (or “reservoir-computing”) and quantum machines (“quantum computing”). We show that UMMs can simulate both types of machines, hence they are both “liquid-” or “reservoir-complete” and “quantum-complete.” Of course, these statements pertain only to the type of problems these machines can solve and not to the amount of resources required for such simulations. Nonetheless, the set-theoretic method presented here provides a general framework which describes the relationship between any computational models. [ABSTRACT FROM AUTHOR]

In this paper, a novel true random bit generator (TRBG) based on a double-scroll attractor is proposed. The double-scroll attractor is obtained from a simple model which is qualitatively similar to Chua's circuit. In order to face the challenge of using the proposed TRBG in cryptography, the proposed TRBG is subjected to statistical tests which are the well-known Federal Information Processing Standards-140-1 and Diehard test suite in the area of cryptography. The proposed TRBG successfully passes all these tests and can be implemented in integrated circuits. [ABSTRACT FROM AUTHOR]

Deep learning is now being used to translate between languages, predict how proteins fold, analyze medical scans, and play games as complex as Go, to name just a few applications of a technique that is now becoming pervasive. Success in those and other realms has brought this machine-learning technique from obscurity in the early 2000s to dominance today. • Although deep learning's rise to fame is relatively recent, its origins are not. In 1958, back when mainframe computers filled rooms and ran on vacuum tubes, knowledge of the interconnections between neurons in the brain inspired Frank Rosenblatt at Cornell to design the first artificial neural network, which he presciently described as a “pattern-recognizing device.” But Rosenblatt's ambitions outpaced the capabilities of his era—and he knew it. Even his inaugural paper was forced to acknowledge the voracious appetite of neural networks for computational power, bemoaning that “as the number of connections in the network increases…the burden on a conventional digital computer soon becomes excessive.” • Fortunately for such artificial neural networks—later rechristened “deep learning” when they included extra layers of neurons—decades of Moore's Law and other improvements in computer hardware yielded a roughly 10-million-fold increase in the number of computations that a computer could do in a second. So when researchers returned to deep learning in the late 2000s, they wielded tools equal to the challenge. [ABSTRACT FROM AUTHOR]

By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von Neumann architecture and the limited signal connections have severely constrained the scalability and performance of such hardware implementations. Recently, many research efforts have been investigated in utilizing the latest discovered memristors in neuromorphic systems due to the similarity of memristors to biological synapses. In this paper, we explore the potential of a memristor crossbar array that functions as an autoassociative memory and apply it to brain-state-in-a-box (BSB) neural networks. Especially, the recall and training functions of a multianswer character recognition process based on the BSB model are studied. The robustness of the BSB circuit is analyzed and evaluated based on extensive Monte Carlo simulations, considering input defects, process variations, and electrical fluctuations. The results show that the hardware-based training scheme proposed in the paper can alleviate and even cancel out the majority of the noise issue. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task. To address this, we propose a novel probabilistic model for multitask learning (MTL) that can automatically balance between low-rank and sparsity constraints. The former assumes a low-rank structure of the underlying predictive hypothesis space to explicitly capture the relationship of different tasks and the latter learns the incoherent sparse patterns private to each task. We derive and perform inference via variational Bayesian methods. Experimental results on both regression and classification tasks on real-world applications demonstrate the effectiveness of the proposed method in dealing with the MTL problems. [ABSTRACT FROM PUBLISHER]

Transfer learning is generally exploited to adapt well-established source knowledge for learning tasks in weakly labeled or unlabeled target domain. Nowadays, it is common to see multiple sources available for knowledge transfer, each of which, however, may not include complete classes information of the target domain. Naively merging multiple sources together would lead to inferior results due to the large divergence among multiple sources. In this paper, we attempt to utilize incomplete multiple sources for effective knowledge transfer to facilitate the learning task in target domain. To this end, we propose an incomplete multisource transfer learning through two directional knowledge transfer, i.e., cross-domain transfer from each source to target, and cross-source transfer. In particular, in cross-domain direction, we deploy latent low-rank transfer learning guided by iterative structure learning to transfer knowledge from each single source to target domain. This practice reinforces to compensate for any missing data in each source by the complete target data. While in cross-source direction, unsupervised manifold regularizer and effective multisource alignment are explored to jointly compensate for missing data from one portion of source to another. In this way, both marginal and conditional distribution discrepancy in two directions would be mitigated. Experimental results on standard cross-domain benchmarks and synthetic data sets demonstrate the effectiveness of our proposed model in knowledge transfer from incomplete multiple sources. [ABSTRACT FROM AUTHOR]

This paper tries to separate fine-grained images by jointly learning the encoding parameters and codebooks through low-rank sparse coding (LRSC) with general and class-specific codebook generation. Instead of treating each local feature independently, we encode the local features within a spatial region jointly by LRSC. This ensures that the spatially nearby local features with similar visual characters are encoded by correlated parameters. In this way, we can make the encoded parameters more consistent for fine-grained image representation. Besides, we also learn a general codebook and a number of class-specific codebooks in combination with the encoding scheme. Since images of fine-grained classes are visually similar, the difference is relatively small between the general codebook and each class-specific codebook. We impose sparsity constraints to model this relationship. Moreover, the incoherences with different codebooks and class-specific codebooks are jointly considered. We evaluate the proposed method on several public image data sets. The experimental results show that by learning general and class-specific codebooks with the joint encoding of local features, we are able to model the differences among different fine-grained classes than many other fine-grained image classification methods. [ABSTRACT FROM AUTHOR]

This paper proposes a new elementary operation approach (EOA) to multidimensional (-D) realization and linear fractional representation (LFR) modeling for multi-input and multi-output (MIMO) -D systems, as an extension of the new EOA proposed for the single-input and single-output (SISO) case by the authors recently. It is shown that, due to the substantial differences between the SISO and MIMO systems, the extension is not straightforward and further significant development is necessary. A matrix relation property among the associated matrices under the augmenting and admissible elementary operations is first revealed. Based on this matrix relation property, the realization problem for the MIMO -D case is formulated as an elementary operation problem of a certain -D polynomial matrix, which makes the extension possible. General constructive procedures are then established for the regular realizations based on the right and left matrix fraction descriptions (MFDs) of a given transfer matrix, respectively, such that one can easily implement this approach by a computer program in, e.g., MATLAB or Maple. Numerical and symbolic examples are provided to illustrate the main ideas and the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

Digital current-mode control is a dual-loop control which potentially results in a better transient response and thus is more favorable than voltage-mode control. There are only a few publications on how to design and implement a fully integrated digital controller as the on-chip implementation is very challenging, especially for current-mode control. This paper addresses those design challenges and considerations. One of the main challenges is to efficiently sample and quantize both the output voltage and inductor current of the buck converter for control purposes. A time-multiplex scheme is used for the control-loop which enables the converter to work with a single ADC. A modified delay-lock-loop DPWM has been developed for minimizing the mismatch of the delay-cells. This enhances the accuracy at high frequency to prevent limit-cycle. A new algorithm has also been proposed for implementing look-up-table digital compensators with 20% less chip area. A converter with the fully integrated digitally controlled loop, including the single ADC, digital compensators and DPWM, has been fabricated in a CMOS 0.35 \mum process with a chip area of 1049 \mum \times\,1533 \mum. Measurement results show that the buck converter has a load transient response of 20 \mus, which is one of the fastest compared to other state-of-the-art digitally controlled buck converter. [ABSTRACT FROM PUBLISHER]

In this paper, we define a class of graph grammars that can be used to model and direct concurrent robotic self-assembly and similar self-organizing processes. We give several detailed examples of the formalism and then focus on the problem of synthesizing a grammar so that it generates a given, prespecified assembly. In particular, to generate an acyclic graph we synthesize a binary grammar (rules involve at most two parts), and for a general graph we synthesize a ternary grammar (rules involve at most three parts). In both cases, we characterize the number of concurrent steps required to achieve the assembly. We also show a general result that implies that no binary grammar can generate a unique stable assembly. We conclude the paper with a discussion of how graph grammars can be used to direct the self-assembly of robotic parts. [ABSTRACT FROM AUTHOR]

In this paper, novel call admission control (CAC) algorithms are developed based on cellular neural networks. These algorithms can achieve high network utilization by performing CAC in real-time, which is imperative in supporting quality of service (QoS) communication over packet-switched networks. The proposed solutions are of basic significance in access technology where a subscriber population (connected to the Internet via an access module) needs to receive services. In this case, QoS can only be preserved by admitting those user configurations which will not overload the access module. The paper treats CAC as a set separation problem where the separation surface is approximated based on a training set. This casts CAC as an image processing task in which a complex admission pattern is to be recognized from a couple of initial points belonging to the training set. Since CNNs can implement any propagation models to explore complex patterns, CAC can then be carried out by a CNN. The major challenge is to find the proper template matrix which yields high network utilization. On the other hand, the proposed method is also capable of handling three-dimensional separation surfaces, as in a typical access scenario there are three traffic classes (e.g., two type of Internet access and one voice over asymmetric digital subscriber line. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel computing model, called probe machine (PM). Unlike the turing machine (TM), PM is a fully parallel computing model in the sense that it can simultaneously process multiple pairs of data, rather than sequentially process every pair of linearly adjacent data. We establish the mathematical model of PM as a nine-tuple consisting of data library, probe library, data controller, probe controller, probe operation, computing platform, detector, true solution storage, and residue collector. We analyze the computation capability of the PM model, and in particular, we show that TM is a special case of PM. We revisit two NP-complete problems, i.e., the graph coloring and Hamilton cycle problems, and devise two algorithms on basis of the established PM model, which can enumerate all solutions to each of these problems by only one probe operation. Furthermore, we show that PM can be implemented by leveraging the nano-DNA probe technologies. The computational power of an electronic computer based on TM is known far more than that of the human brain. A question naturally arises: will a future computer based on PM outperform the human brain in more ways beyond the computational power? [ABSTRACT FROM AUTHOR]

There is a growing interest in quantum computers and quantum algorithm development. It has been proved that ideal quantum computers, with zero error rates and large decoherence times, can solve problems that are intractable for today’s classical computers. Quantum computers use two resources, superposition and entanglement, that have no classical analog. Since quantum computer platforms that are currently available comprise only a few dozen of qubits, the use of quantum simulators is essential in developing and testing new quantum algorithms. We present a novel quantum simulator based on memristor crossbar circuits and use them to simulate well-known quantum algorithms, namely the Deutsch and Grover quantum algorithms. In quantum computing the dominant algebraic operations are matrix-vector multiplications. The execution time grows exponentially with the simulated number of qubits, causing an exponential slowdown in quantum algorithm execution using classical computers. In this work, we show that the inherent characteristics of memristor arrays can be used to overcome this problem and that memristor arrays can be used not only as independent quantum simulators but also as a part of a quantum computer stack where classical computers accelerators are connected. Our memristive crossbar circuits are re-configurable and can be programmed to simulate any quantum algorithm. [ABSTRACT FROM AUTHOR]

Increasingly, many machine learning applications are now associated with very large data sets whose sizes were almost unimaginable just a short time ago. As a result, many of the current algorithms cannot handle, or do not scale to, today’s extremely large volumes of data. Fortunately, not all features that make up a typical data set carry information that is relevant or useful for prediction, and identifying and removing such irrelevant features can significantly reduce the total data size. The unfortunate dilemma, however, is that some of the current data sets are so large that common feature selection algorithms—whose very goal is to reduce the dimensionality—cannot handle such large data sets, creating a vicious cycle. We describe a sequential learning framework for feature subset selection (SLSS) that can scale with both the number of features and the number of observations. The proposed framework uses multiarm bandit algorithms to sequentially search a subset of variables, and assign a level of importance for each feature. The novel contribution of SLSS is its ability to naturally scale to large data sets, evaluate such data in a very small amount of time, and be performed independently of the optimization of any classifier to reduce unnecessary complexity. We demonstrate the capabilities of SLSS on synthetic and real-world data sets. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of globally uniformly exponentially stabilizing a linear system to the origin by output feedback while avoiding a prescribed set of input values. We consider that the set of input values to avoid is given by the union of a finite number of closed polytopes that do not contain the origin and we refer to this restriction as a reverse polytopic input constraint. We show that the synthesis of the hybrid controller can be performed by solving a set of linear matrix inequalities for the full-state feedback case, and by solving a set of bilinear matrix inequalities for the output feedback case. The resulting closed-loop hybrid system is shown to satisfy key conditions for well-posedness and robustness to small measurement noise. Furthermore, we apply the proposed hybrid controller to the stabilization of a single-input linear system subject to reverse polytopic constraints on the norm of the input. The behavior of the corresponding closed-loop hybrid system is validated by simulations. [ABSTRACT FROM PUBLISHER]

The paper proposes a control-theoretic framework for verification of numerical software systems, and puts forward software verification as an important application of control and systems theory. The idea is to transfer Lyapunov functions and the associated computational techniques from control systems analysis and convex optimization to verification of various software safety and performance specifications. These include but are not limited to absence of overflow, absence of division-by-zero, termination in finite time, absence of dead-code, and certain user-specified assertions. Central to this framework are Lyapunov invariants. These are properly constructed functions of the program variables, and satisfy certain properties—analogous to those of Lyapunov functions—along the execution trace. The search for the invariants can be formulated as a convex optimization problem. If the associated optimization problem is feasible, the result is a certificate for the specification. [ABSTRACT FROM AUTHOR]

Scheduling policies that favor small jobs have received growing attention due to their superior performance with respect to mean delay, e.g., Shortest Remaining Processing Time (SRPT) and Preemptive Shortest Job First (PSJF). In this paper, we study the delay distribution of a generalization of the class of scheduling policies called SMART (because policies in it have “SMAll Response Times”), which includes SRPT, PSJF, and a range of practical variants, in a discrete-time queueing system under the many sources large deviations regime. [ABSTRACT FROM AUTHOR]

Coordinate Rotation DIgital Computer (CORDIC) rotator is a well known and widely used algorithm within computers due to its way of carrying out some calculations such as trigonometric functions, among others. A scale factor compensation inherent to the CORDIC algorithm becomes an important drawback when trying to improve its benefits, although some authors have come up with a new scaling-free version, which has been successfully implemented within wireless applications. However, this new CORDIC can still be significantly improved by modifying some of its parts, therefore, this paper shows an enhanced version of the scaling-free CORDIC. These new enhancements have been implemented and tested, obtaining some new architectures which are able to reach a 35% lower latency and a 36% reduction in area and power consumption compared to the original scaling-free architecture. [ABSTRACT FROM AUTHOR]

The problem of efficiently steering dynamical systems by generating input plans is considered. Plans are considered which consist of finite-length words constructed on an alphabet of input symbols, which could be, e.g., transmitted through a limited capacity channel to a remote system, where they can be decoded in suitable control actions. Efficiency is considered in terms of the computational complexity of plans, and in terms of their description length (in number of bits). We show that, by suitable choice of the control encoding, finite plans can be efficiently built for a wide class of dynamical systems, computing arbitrarily close approximations of a desired equilibrium in polynomial time. The paper also investigates how the efficiency of planning is affected by the choice of inputs, and provides some results as to optimal performance in terms of accuracy and range. [ABSTRACT FROM AUTHOR]

The coordinate rotational digital computer (CORDIC) algorithm is a well-known iterative arithmetic for performing vector rotations in many digital signal processing (DSP) applications. However, the large number of iteration is a major disadvantage of this algorithm for its speed performance. Many researchers have proposed schemes to reduce the number of iterations. Nevertheless, in performing the existing CORDIC algorithms, the norm of the vector is usually enlarged so that extra scaling operations are required to deliver the normalized output. In this paper, we merge the two operation phases (microrotations and scaling phases) and propose a new vector rotational scheme called mixed-scaling-rotation coordinate rotational digital computer (MSR-CORDIC) algorithm. It can eliminate the overhead of the scaling operations that are inevitable in existing CORDIC algorithms; hence, it can significantly reduce the total iteration number so as to improve the speed performance. The proposed MSR-CORDIC can be applied to DSP applications, in which the I rotational angles are known in advance [e.g., twiddle factor in fast Fourier transform (FFT) processor designs]. Moreover, most CORDIC algorithms generally suffer from the roundoff noise in the fixed-wordlength implementations. We also propose two schemes to control and reduce the impairment. Our simulation results show that the MSR-CORDIC algorithm can enhance the signal-to-quantization-noise ratio (SQNR) performance by controlling the internal dynamic range. We also investigate the first- and second-order statistical properties, including the mean and variance of the SQNR. Simulation results show that the MSR-CORDIC can enhance SQNR performance of both first- and second-order statistical properties. At the VLSI architecture level, we proposed a generalized MSR-CORDIC engine for the tradeoff between hardware complexity and quantization error performance. It can further reduce the hardware complexity when compared with the newly proposed extend elementary angle set CORDIC algorithm [5]. The MSR-CORDIC scheme has been applied to a variable-length FFT processor design [29], and results in significant hardware reduction in implementing the twiddle factor operations. [ABSTRACT FROM AUTHOR]

Painful exercises in basic arithmetic are a vivid part of our elementary school memories. A multiplication like 3,752 ? 6,901 carried out with just pencil and paper for assistance may well take up to a minute. Of course, today, with a cellphone always at hand, we can quickly check that the result of our little exercise is 25,892,552. Indeed, the processors in modern cellphones can together carry out more than 100 billion such operations per second. What's more, the chips consume just a few watts of power, making them vastly more efficient than our slow brains, which consume about 20 watts and need significantly more time to achieve the same result. Of course, the brain didn't evolve to perform arithmetic. So it does that rather badly. But it excels at processing a continuous stream of information from our surroundings. And it acts on that information-sometimes far more rapidly than we're aware of. No matter how much energy a conventional computer consumes, it will struggle with feats the brain finds easy, such as understanding language and running up a flight of stairs. [ABSTRACT FROM AUTHOR]

To the best of our knowledge, we present the first hardware implementation of isogeny-based cryptography available in the literature. Particularly, we present the first implementation of the supersingular isogeny Diffie-Hellman (SIDH) key exchange, which features quantum-resistance. We optimize this design for speed by creating a high throughput multiplier unit, taking advantage of parallelization of arithmetic in \mathbb Fp^{2} , and minimizing pipeline stalls with optimal scheduling. Consequently, our results are also faster than software libraries running affine SIDH even on Intel Haswell processors. For our implementation at 85-bit quantum security and 128-bit classical security, we generate ephemeral public keys in 1.655 million cycles for Alice and 1.490 million cycles for Bob. We generate the shared secret in an additional 1.510 million cycles for Alice and 1.312 million cycles for Bob. On a Virtex-7, these results are approximately 1.5 times faster than known software implementations running the same 512-bit SIDH. Our results and observations show that the isogeny-based schemes can be implemented with high efficiency on reconfigurable hardware. [ABSTRACT FROM AUTHOR]

Three distinct methods of reading multi-level cross-point resistive states from selector-less RRAM arrays are implemented in a physical system and compared for read-out accuracy. They are: the standard, direct measurement method and two methods that attempt to enhance accuracy by computing cross-point resistance on the basis of multiple measurements. Results indicate that the standard method performs as well as or better than its competitors. SPICE simulations are then performed with controlled amounts of non-idealities introduced in the system in order to test whether any technique offers particular resilience against typical practical imperfections such as crossbar line resistance. We conclude that even though certain non-idealities are shown to be minimized by different circuit-level read-out strategies, line resistance within the crossbar remains an outstanding challenge. [ABSTRACT FROM PUBLISHER]

BEFORE SILICON GOT ITS OWN VALLEY, THIS mild-mannered element had to vanquish many other contenders to prove itself the premier semiconductor technology. It did so in the 1950s and 1960s. Today, carbon is poised at a similar crossroads, with carbon-based technologies on the verge of transforming computing and boosting batterystorage capacities. Already, researchers have used these technologies to demonstrate paper-thin batteries, unbreakable touch screens, and terabit-speed wireless communications And on the farther horizon they envision such carbon-enabled wonders as space elevators, filters that can make seawater drinkable, bionic organs, and transplantable neurons. Whatever miracles emerge from Carbon Valley, its carbon-techtitans will surely think fondly upon their field's founding mother, Mildred Dresselhaus. This MIT professor of physics and engineering has, since the early 1960s, been laying the groundwork for networks of nanometerscale carbon sheets, lattices, wires, and switches. Future engineers will turn these things, fabricated from carbon-based materials such as graphene, into the systems that will carry computing into its next era. [ABSTRACT FROM PUBLISHER]

We formulate notions of opacity for cyberphysical systems modeled as discrete-time linear time-invariant systems. A set of secret states is $k$ -ISO with respect to a set of nonsecret states if, starting from these sets at time 0, the outputs at time $k$ are indistinguishable to an adversarial observer. Necessary and sufficient conditions to ensure that a secret specification is $k$ -ISO are established in terms of sets of reachable states. We also show how to adapt techniques for computing underapproximations and overapproximations of the set of reachable states of dynamical systems in order to soundly approximate $k$ -ISO. Furthermore, we provide a condition for output controllability, if $k$ -ISO holds, and show that the converse holds under an additional assumption. We extend the theory of opacity for single-adversary systems to the case of multiple adversaries and develop several notions of decentralized opacity. We study the following scenarios: first, the presence or lack of a centralized coordinator, and, second, the presence or absence of collusion among adversaries. In the case of colluding adversaries, we derive a condition for nonopacity that depends on the structure of the directed graph representing the communication between adversaries. Finally, we relax the condition that the outputs be indistinguishable and define a notion of $\epsilon$ -opacity, and also provide an extension to the case of nonlinear systems. [ABSTRACT FROM AUTHOR]

A band-limited signal can be recovered from its periodic nonuniformly spaced samples provided the average sampling rate is at least the Nyquist rate. A multirate filter bank structure is used to both model this nonuniform sampling (through the analysis bank) and reconstruct a uniformly sampled sequence (through the synthesis bank). Several techniques for modeling the nonuniform sampling are presented for various cases of sampling. Conditions on the filter bank structure are used to accurately reconstruct uniform samples of the input signal at the Nyquist rate. Several examples and simulation results are presented, with emphasis on forms of nonuniform sampling that may be useful in mixed-signal integrated circuits. [ABSTRACT FROM AUTHOR]

We outline a model of computing with high-dimensional (HD) vectors—where the dimensionality is in the thousands. It is built on ideas from traditional (symbolic) computing and artificial neural nets/deep learning, and complements them with ideas from probability theory, statistics, and abstract algebra. Key properties of HD computing include a well-defined set of arithmetic operations on vectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operation, making it possible to develop efficient algorithms for large-scale real-world tasks. We present a 2-D architecture and demonstrate its functionality with examples from text analysis, pattern recognition, and biosignal processing, while achieving high levels of classification accuracy (close to or above conventional machine-learning methods), energy efficiency, and robustness with simple algorithms that learn fast. HD computing is ideally suited for 3-D nanometer circuit technology, vastly increasing circuit density and energy efficiency, and paving a way to systems capable of advanced cognitive tasks. [ABSTRACT FROM PUBLISHER]

Consumer technology is often a story of revolutionary leaps followed by a descent into familiarity. The first computers advanced so quickly that new models went obsolete while they were still on store shelves. Today, any US $500 laptop will be relevant for a decade. A similar story can be told of smartphones, TVs, even cars. [ABSTRACT FROM AUTHOR]

In the literature, the generalized $(a, b)$- way Karatsuba algorithm (KA) is a well-known high-precision technique for implementing a subquadratic digit-serial multiplication. We present here the corrections to the space and complexities pertaining to addition operations in $(a, b)$-way KA approach. [ABSTRACT FROM PUBLISHER]

Paper punch cards, each representing a line of code or data, were how programmers got information into computers from the 1950s on. [ABSTRACT FROM PUBLISHER]

Differentiable neural computers (DNCs) extend artificial neural networks with an explicit memory without interference, thus enabling the model to perform classic computation tasks, such as graph traversal. However, such models are difficult to train, requiring long training times and large datasets. In this work, we achieve some of the computational capabilities of DNCs with a model that can be trained very efficiently, namely, an echo state network with an explicit memory without interference. This extension enables echo state networks to recognize all regular languages, including those that contractive echo state networks provably cannot recognize. Furthermore, we demonstrate experimentally that our model performs comparably to its fully trained deep version on several typical benchmark tasks for DNCs. [ABSTRACT FROM AUTHOR]

Immediately following the Second World War, electrical engineers grappled with a fundamental but open question: How should electronic digital computers be built? What kind of switch would serve best for logic circuits? And what should be used for main memory? [ABSTRACT FROM AUTHOR]

An easy practical algebraic algorithm was proposed for partial expansion of rational functions with multiple high-order poles. The simple recursive implementation of the proposed method involves neither long division nor differentiation and requires only elementary arithmetic operations. It is suitable for computer or hand calculation of partial expansion of both proper and improper functions with multiple high-order poles with desirable accuracy. [ABSTRACT FROM AUTHOR]

State-based notions of opacity, such as initial-state opacity and infinite-step opacity, emerge as key properties in numerous security applications of discrete event systems. We consider systems that are modeled as partially observed nondeterministic finite automata and tackle the problem of constructing a minimally restrictive opacity-enforcing supervisor (MOES), which limits the system's behavior within some prespecified legal behavior while enforcing initial-state opacity or infinite-step opacity requirements. We characterize the solution to MOES, under some mild assumptions, in terms of the supremal element of certain controllable, normal, and opaque languages. We also show that this supremal element always exists and that it can be implemented using state estimators. The result is a supervisor that achieves conformance to the pre-specified legal behavior while enforcing initial-state opacity by disabling, at any given time, a subset of the controllable system events, in a way that minimally restricts the range of allowable system behavior. Although infinite-step opacity cannot be easily translated to language-based opacity, we show that, by using a finite bank of supervisors, the aforementioned approach can be extended to enforce infinite-step opacity in a minimally restrictive way. [ABSTRACT FROM PUBLISHER]

Recently,a neural dynamical approach to solving linearly constrained variational inequality problems is presented, and its stability and convergence are conjectured by simulation. This technical note analyzes the global stability and convergence of the neural dynamical approach. Theoretically, it is shown that the neural dynamical approach is convergent globally to a solution when the nonlinear mapping is monotone at the solution. Unlike existing convergence results of neural dynamical methods for solving linearly or nonlinearly variational inequalities, our main results don't assume the differentiability condition of the nonlinear mapping. Therefore, the neural dynamical approach can be further guaranteed to solve linearly constrained monotone variational inequality problems with a non-smooth mapping. Comparisons and examples illustrative significance of the obtained results on non-smooth mapping. [ABSTRACT FROM AUTHOR]

For high-speed plane-wave filtering applications, real-time 2-D spatio-temporal linear-array broadband beam filters are required, operating at temporal frame rates in excess of hundreds of megahertz. The corresponding application specific VLSI circuits must have low critical-path latencies. A novel high-speed systolic array architecture for a first-order 2-D broadband frequency-planar spatio-temporal beam filter is proposed for this purpose and employs a field-programmable gate array (FPGA) circuit where the critical path latency is minimized by timing optimization of inter- and intra-parallel processor pipelines, together with 3-D look-ahead techniques. The method facilitates single-chip VLSI circuit implementations operating at real-time frame rates of several hundred megahertz. [ABSTRACT FROM AUTHOR]

A novel direct digital frequency synthesis (DDFS) architecture based on the differential CORDIC (DCORDIC) algorithm is presented. The architecture allows digit-level pipelining in the CORDIC angle path by implementing a two-dimensional systolic array. Unlike other DDFS architectures, it incorporates the phase accumulator in the digit-level pipelining framework so that a bottleneck-free datapath throughout the whole system is achieved in a scalable manner. A generic environment that generates fully synthesizable Verilog codes that implement the proposed architecture is created and the physical attributes of the resulting system are discussed. [ABSTRACT FROM AUTHOR]

An algorithm for architecture-level exploration of the ΔΣ A/D converter (ADC) design space is presented. Starting from the desired specification, the algorithm finds an optimal solution by exhaustively exploring both single-loop and cascaded architectures, with a single-bit or multibit quantizer, for a range of oversampling ratios. A fast filter-level step evaluates the performance of all loop-filter topologies and passes the accepted solutions to the architecture-level optimization step which maps the filters on feasible architectures and evaluates their performance. The power consumption of each accepted architecture is estimated and the best top-ten solutions in terms of the ratio of peak signal-to-noise+distortion ratio versus power consumption are further optimized for yield. Experimental results for two different design targets are presented. They show that previously published solutions are among the best architectures for a given target but that better solutions can be designed as well. [ABSTRACT FROM AUTHOR]

The Supersingular Isogeny Key Encapsulation mechanism (SIKE) is the only post-quantum key encapsulation protocol based on elliptic curves and isogeny maps between them. Despite the quantum security of the protocol, SIKE requires a greater number of clock cycles and hence does not provide competitive timing and energy consumption results. However, it is more attractive offering the smallest public key as well as ciphertext sizes, which considering the impact of the communication costs and storage of the keys could become a good fit for resource-constrained devices. In this work, we present the fastest practical implementation of SIKE, targeting the platform Cortex-M4 based on the ARMv7-M architecture. We performed our measurements on the STM32F407VG microcontroller for benchmarking the clock cycles and on Nucleo-F411RE attached to X-NUCLEO-LPM01A (Power Shield) for measuring the energy consumption of the protocol. The low-level finite field arithmetic operations play main role in determining the efficiency of SIKE. Therefore, we mainly focus on their optimization and apply them to all NIST-required security levels. Our SIKEp434 implementation for NIST security level 1 is about 22.97% faster than the counterparts appeared in Seo et al. (2020), where for the SIKEp503, SIKEp610 and SIKEp751 the speedup reaches 21.10%, 19.21% and 19.08%. Finally, we benchmark energy consumption and report optimization of up to 11.9% depending on the NIST security level implementation. [ABSTRACT FROM AUTHOR]

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars. [ABSTRACT FROM AUTHOR]

The necessary and sufficient conditions for (strict) positive realness of strictly proper transfer functions are derived in the above-mentioned paper refid="ref1"/. Presenting a counter-example, we show that there are inconsistencies between definitions stated in refid="sec2"Section II and main results proved in section III of refid="ref1"/. Also, the required corrections in the main results of refid="ref1"/ are presented. Note however that the results in refid="ref1"/ are correct for the definition of SPR in the Circuit Theory sense. [ABSTRACT FROM AUTHOR]

The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a “quantum advantage,” once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work, over the past three decades, we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here, we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results. [ABSTRACT FROM AUTHOR]