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Next-generation wireless networks strive for higher communication rates, ultra-low latency, seamless connectivity, and high-resolution sensing capabilities. To meet these demands, terahertz (THz)-band signal processing is envisioned as a key technology offering wide bandwidth and sub-millimeter wavelength. Furthermore, THz integrated sensing and communications (ISAC) paradigm has emerged jointly access spectrum and reduced hardware costs through a unified platform. To address the challenges in THz propagation, THz-ISAC systems employ extremely large antenna arrays to improve the beamforming gain for communications with high data rates and sensing with high resolution. However, the cost and power consumption of implementing fully digital beamformers are prohibitive. While hybrid analog/digital beamforming can be a potential solution, the use of subcarrier-independent analog beamformers leads to the beam-squint phenomenon where different subcarriers observe distinct directions because of adopting the same analog beamformer across all subcarriers. In this paper, we develop a sparse array architecture for THz-ISAC with hybrid beamforming to provide a cost-effective solution. We analyze the antenna selection problem under beam-squint influence and introduce a manifold optimization approach for hybrid beamforming design. To reduce computational and memory costs, we propose novel algorithms leveraging grouped subarrays, quantized performance metrics, and sequential optimization. These approaches yield a significant reduction in the number of possible subarray configurations, which enables us to devise a neural network with classification model to accurately perform antenna selection., 14pages10figures, submitted to IEEE

Non-terrestrial networks (NTNs) are considered one of the key enablers in sixth-generation (6G) wireless networks; and with their rapid growth, system-level metrics analysis adds crucial understanding into NTN system performance. Applying stochastic geometry (SG) as a system-level analysis tool in the context of NTN offers novel insights into the network tradeoffs. In this paper, we study and highlight NTN common system-level metrics from three perspectives: NTN platform types, typical communication issues, and application scenarios. In addition to summarizing existing research, we study the best-suited SG models for different platforms and system-level metrics which have not been well studied in the literature. In addition, we showcase NTN-dominated prospective application scenarios. Finally, we carry out a performance analysis of system-level metrics for these applications based on SG models., Comment: 7 pages

Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices. The recent rapid increase of edge computing devices has led to an active search for techniques to address the above-mentioned limitations of machine learning frameworks. The quantization of artificial neural networks (ANNs), which converts the full-precision synaptic weights into low-bit versions, emerged as one of the solutions. At the same time, spiking neural networks (SNNs) have become an attractive alternative to conventional ANNs due to their temporal information processing capability, energy efficiency, and high biological plausibility. Despite being driven by the same motivation, the simultaneous utilization of both concepts has yet to be thoroughly studied. Therefore, this work aims to bridge the gap between recent progress in quantized neural networks and SNNs. It presents an extensive study on the performance of the quantization function, represented as a linear combination of sigmoid functions, exploited in low-bit weight quantization in SNNs. The presented quantization function demonstrates the state-of-the-art performance on four popular benchmarks, CIFAR10-DVS, DVS128 Gesture, N-Caltech101, and N-MNIST, for binary networks (64.05\%, 95.45\%, 68.71\%, and 99.43\% respectively) with small accuracy drops and up to 31$\times$ memory savings, which outperforms existing methods., Comment: 8 pages, 5 Figures, accepted at IJCNN'23

To efficiently utilize the wireless spectrum and save hardware costs, the fifth generation and beyond (B5G) wireless networks envisage integrated sensing and communications (ISAC) paradigms to jointly access the spectrum. In B5G systems, the expensive hardware is usually avoided by employing hybrid beamformers that employ fewer radio-frequency chains but at the cost of the multiplexing gain. Recently, it has been proposed to overcome this shortcoming of millimeter wave (mmWave) hybrid beamformers through spatial path index modulation (SPIM), which modulates the spatial paths between the base station and users and improves spectral efficiency. In this paper, we propose an SPIM-ISAC approach for hybrid beamforming to simultaneously generate beams toward both radar targets and communications users. We introduce a low complexity approach for the design of hybrid beamformers, which include radar-only and communications-only beamformers. Numerical experiments demonstrate that our SPIM-ISAC approach exhibits a significant performance improvement over the conventional mmWave-ISAC design in terms of spectral efficiency and the generated beampattern., Accepted paper in 2023 IEEE Radar Conference

Empowered by the backpropagation (BP) algorithm, deep neural networks have dominated the race in solving various cognitive tasks. The restricted training pattern in the standard BP requires end-to-end error propagation, causing large memory cost and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacle by completely cutting off the backward path between modules and isolating their gradients to reduce memory cost and accelerate the training process. These methods prevent errors from flowing between modules and hence information exchange, resulting in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces inter-module backward dependency and allows errors to flow between modules. The algorithm facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, in ResNet32 with 16 local modules, our method surpasses the conventional greedy local training method by 4.00\% and a recent work by 1.83\% in accuracy on CIFAR10, respectively. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79\% reduction in memory cost and 52\% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms towards better efficiency and biological plausibility.

Content Addressable Memories (CAMs) are considered a key-enabler for in-memory computing (IMC). IMC shows order of magnitude improvement in energy efficiency and throughput compared to traditional computing techniques. Recently, analog CAMs (aCAMs) were proposed as a means to improve storage density and energy efficiency. In this work, we propose two new aCAM cells to improve data encoding and robustness as compared to existing aCAM cells. We propose a methodology to choose the margin and interval width for data encoding. In addition, we perform a comprehensive comparison against prior work in terms of the number of intervals, noise sensitivity, dynamic range, energy, latency, area, and probability of failure., This is a revised manuscript that is under consideration for publication at IEEE TCAS-I

This paper introduces a reconfigurable intelligent surface (RIS)-assisted grant-free non-orthogonal multiple-access (GF-NOMA) scheme. To ensure the power reception disparity required by the power domain NOMA (PD-NOMA), we propose a joint user clustering and RIS assignment/alignment approach that maximizes the network sum rate by judiciously pairing user equipments (UEs) with distinct channel gains, assigning RISs to proper clusters, and aligning RIS phase shifts to the cluster members yielding the highest cluster sum rate. Once UEs are acknowledged with the cluster index, they are allowed to access their resource blocks (RBs) at any time requiring neither further grant acquisitions from the base station (BS) nor power control as all UEs are requested to transmit at the same power. In this way, the proposed approach performs an implicit over-the-air power control with minimal control signaling between BS and UEs, which has shown to deliver up to 20% higher network sum rate than benchmark GF-NOMA and optimal grant-based PD-NOMA schemes depending on the network parameters. The given numerical results also investigate the impact of UE density, RIS deployment, and RIS hardware specifications on the overall performance of the proposed RIS-aided GF-NOMA scheme.

The higher speed, scalability and parallelism offered by ReRAM crossbar arrays foster development of ReRAM-based next generation AI accelerators. At the same time, sensitivity of ReRAM to temperature variations decreases R_on/Roff ratio and negatively affects the achieved accuracy and reliability of the hardware. Various works on temperature-aware optimization and remapping in ReRAM crossbar arrays reported up to 58\% improvement in accuracy and 2.39$\times$ ReRAM lifetime enhancement. This paper classifies the challenges caused by thermal heat, starting from constraints in ReRAM cells' dimensions and characteristics to their placement in the architecture. In addition, it reviews available solutions designed to mitigate the impact of these challenges, including emerging temperature-resilient DNN training methods. Our work also provides a summary of the techniques and their advantages and limitations., Comment: 18 pages

DNA pattern matching is essential for many widely used bioinformatics applications. Disease diagnosis is one of these applications, since analyzing changes in DNA sequences can increase our understanding of possible genetic diseases. The remarkable growth in the size of DNA datasets has resulted in challenges in discovering DNA patterns efficiently in terms of run time and power consumption. In this paper, we propose an efficient hardware and software codesign that determines the chance of the occurrence of repeat-expansion diseases using DNA pattern matching. The proposed design parallelizes the DNA pattern matching task using associative memory realized with analog content-addressable memory and implements an algorithm that returns the maximum number of consecutive occurrences of a specific pattern within a DNA sequence. We fully implement all the required hardware circuits with PTM 45-nm technology, and we evaluate the proposed architecture on a practical human DNA dataset. The results show that our design is energy-efficient and significantly accelerates the DNA pattern matching task compared to previous approaches described in the literature.

The bottleneck between the processor and memory is the most significant barrier to the ongoing development of efficient processing systems. Therefore, a research effort begun to shift from process-orcentric architectures to memory-centric architectures. Various in-memory processor architectures have been proposed to break this barrier to pave the way for ever-demanding memory-bound applications. Associative in-memory processing is a successful candidate for truly in-memory computing, in which processor and memory are combined in the same location to eliminate the expensive data access costs. The architecture exhibits an unmatched advantage for data-intensive applications due to its memory-centric design principles. On the other hand, this advantage can be revealed fully by an efficient design methodology. This study puts further progressive effort by proposing a hardware/software design methodology for associative in-memory processors. The methodology aims to decrease energy consumption and area requirement of the processor architecture specifically programmed to perform a given task. According to the evaluation of nine different benchmarks, such as fast Fourier transform and multiply-accumulate, the proposed design flow accomplishes an average similar to 7% reduction in memory area and similar to 18% savings in total energy consumption. (C) 2021 Elsevier Inc. All rights reserved.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. {However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation.} The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance. Simulation results demonstrate that the proposed DL-based schemes significantly outperform conventional orthogonal matching pursuit (OMP) techniques in terms of the normalized mean-squared error (NMSE), computational complexity, and spectral efficiency, particularly in the low signal-to-noise ratio regime. When compared to OMP approaches that achieve an NMSE gap of \$\unit[\{4-10\}]{dB}\$ with respect to the Cramer Rao Lower Bound (CRLB), the proposed algorithms reduce the CRLB gap to only \$\unit[\{1-1.5\}]{dB}\$, while significantly reducing complexity by two orders of magnitude., 16 pages, 8 figures, submitted to IEEE transactions on wireless communications. arXiv admin note: text overlap with arXiv:1704.08572 by other authors

In-memory associative processor architectures are offered as a great candidate to overcome memory-wall bottleneck and to enable vector/parallel arithmetic operations. In this paper, we extend the functionality of the associative processor to multi-valued arithmetic. To allow for in-memory compute implementation of arithmetic or logic functions, we propose a structured methodology enabling the automatic generation of the corresponding look-up tables (LUTs). We propose two approaches to build the LUTs: a first approach that formalizes the intuition behind LUT pass ordering and a more optimized approach that reduces the number of required write cycles. To demonstrate these methodologies, we present a novel ternary associative processor (TAP) architecture that is employed to implement efficient ternary vector in-place addition. A SPICE-MATLAB co-simulator is implemented to test the functionality of the TAP and to evaluate the performance of the proposed AP ternary in-place adder implementations in terms of energy, delay, and area. Results show that compared to the binary AP adder, the ternary AP adder results in a 12.25\% and 6.2\% reduction in energy and area, respectively. The ternary AP also demonstrates a 52.64\% reduction in energy and a delay that is up to 9.5x smaller when compared to a state-of-art ternary carry-lookahead adder.

This letter investigates a non-orthogonal multiple access (NOMA) assisted millimeter-wave device-to-device (D2D) network practically limited by multiple interference noises, transceiver hardware impairments, imperfect successive interference cancellation, and channel state information mismatch. Generalized outage probability expressions for NOMA-D2D users are deduced and achieved results, validated by Monte Carlo simulations, are compared with the orthogonal multiple access to show the superior performance of the proposed network model, 4 pages, 3 figures

Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn using gradient-descent in situ is still missing. In this paper, we propose a local, gradient-based, error-triggered learning algorithm with online ternary weight updates. The proposed algorithm enables online training of multi-layer SNNs with memristive neuromorphic hardware showing a small loss in the performance compared with the state of the art. We also propose a hardware architecture based on memristive crossbar arrays to perform the required vector-matrix multiplications. The necessary peripheral circuitry including pre-synaptic, post-synaptic and write circuits required for online training, have been designed in the sub-threshold regime for power saving with a standard 180 nm CMOS process., Comment: 15 pages, 11 figures, Journal of Emerging Technology in Circuits and Systems (JETCAS)

Recent breakthroughs suggest that local, approximate gradient descent learning is compatible with Spiking Neural Networks (SNNs). Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn in-situ as accurately as conventional processors is still missing. Here, we propose a subthreshold circuit architecture designed through insights obtained from machine learning and computational neuroscience that could achieve such accuracy. Using a surrogate gradient learning framework, we derive local, error-triggered learning dynamics compatible with crossbar arrays and the temporal dynamics of SNNs. The derivation reveals that circuits used for inference and training dynamics can be shared, which simplifies the circuit and suppresses the effects of fabrication mismatch. We present SPICE simulations on XFAB 180nm process, as well as large-scale simulations of the spiking neural networks on event-based benchmarks, including a gesture recognition task. Our results show that the number of updates can be reduced hundred-fold compared to the standard rule while achieving performances that are on par with the state-of-the-art.

Transistor-based memories are rapidly approaching their maximum density per unit area. Resistive crossbar arrays enable denser memory due to the small size of switching devices. However, due to the resistive nature of these memories, they suffer from current sneak paths complicating the readout procedure. In this paper, we propose a row readout technique with circuitry that can be used to read {selector-less} resistive crossbar based memories. High throughput reading and writing techniques are needed to overcome the memory-wall bottleneck problem and to enable near memory computing paradigm. The proposed technique can read the entire row of dense crossbar arrays in one cycle, unlike previously published techniques. The requirements for the readout circuitry are discussed and satisfied in the proposed circuit. Additionally, an approximated expression for the power consumed while reading the array is derived. A figure of merit is defined and used to compare the proposed approach with existing reading techniques. Finally, a quantitative analysis of the effect of biasing mismatch on the array size is discussed.

International audience

International audience

İslami bankacılık, geçen yüzyılın yedinci yılında İslam dünyasında ortaya çıkan yeni finansal akımlardan biridir. Malezya gibi öncü ilçelerden ortaya çıkan İslami mali konsept, İslam hukukuna uymak için birçok stratejiyi ayarlamak zorundaydı. İslam hukukunun finansal talimatlarının kalbi, sermaye sağlayıcı ile borçlu arasında çıkar ve kâr ve zarar paylaşımının yasaklanmasıdır. Bununla birlikte, böyle bir konsept, İslami bankacılık kavramını benimseyen finansal enstitünün risk yönetimi üzerinde birçok etkiye neden olmaktadır. İslami bankacılıkta kredi, piyasa, likidite ve operasyonel riskler de dahil olmak üzere geleneksel risk türleri geçerli olmakla birlikte, risk kaynakları ve azaltma stratejileri geleneksel bankacılık ile karşılaştırıldığında farklılık arz etmektedir. Ayrıca İslami bankacılığa eşlik eden, getiri oranı, hisse senedi yatırım riski, şeriat uyumsuzluk riski ve yerinden olmuş ticari risk gibi benzersiz risk türleri de vardır. Pratikte, İslami bankacılık ilkelerini benimseyen Libya bankalarında risk yönetimine ilişkin bir vaka incelemesi bir teşhis araştırması olarak değerlendirildi. Çalışmanın sonuçları, finansal olmayan birçok faktörden etkilenen ülkedeki risk yönetimi kavramındaki olgunlaşmamışlığı göstermektedir. Bu nedenle, araştırmacı Libya Bankalarındaki risk yönetimi konseptinin gelişimini güçlendirmek için tavsiyesini sunar. Islamic banking is one of the new growing financial streams that emerged in the Islamic world in the seventh decade of last century. Emerging from pioneering counties such as Malaysia, the Islamic financial concept had to adjust many of the strategies in order to comply with the Islamic law. The heart of the Islamic law's financial instructions is the prohibition of interest and the sharing of profit and losses between the capital provider and the borrower. However, such a concept imposes many implications on the risk management of the financial institute adopting the Islamic banking concept. While traditional risk types, including credit, market, liquidity and operational risks, apply in Islamic banking, the sources of risk and mitigation strategies differ in comparison with conventional banking. Furthermore, there are unique risk types that accompany Islamic banking such as rate of return risk, equity investment risk, Sharia non-compliance risk, and displaced commercial risk. On the practical side, a case study of risk management in Libyan banks adopting Islamic banking principles is evaluated as a diagnostic research. The outcomes of the study show immaturity in the concept of risk management in the country affected by many non-financial factors. Therefore, the researcher provides his recommendation in order to empower development of the risk management concept in Libyan Banks. 104

Full-duplex systems are expected to achieve 100% rate improvement over half-duplex systems if the self-interference signal can be significantly mitigated. In this paper, we propose the first full-duplex system utilizing Multi-Reconfigurable Antenna (MRA) with ?90% rate improvement compared to half-duplex systems. MRA is a dynamically reconfigurable antenna structure, that is capable of changing its properties according to certain input configurations. A comprehensive experimental analysis is conducted to characterize the system performance in typical indoor environments. The experiments are performed using a fabricated MRA that has 4096 configurable radiation patterns. The achieved MRA-based passive self-interference suppression is investigated, with detailed analysis for the MRA training overhead. In addition, a heuristic-based approach is proposed to reduce the MRA training overhead. The results show that at 1% training overhead, a total of 95dB self-interference cancellation is achieved in typical indoor environments. The 95dB self-interference cancellation is experimentally shown to be sufficient for 90% full-duplex rate improvement compared to half-duplex systems., Submitted to IEEE Transactions on Wireless Communications

International audience

Cognitive Radio (CR) is capable of adaptive learning and reconfiguration, promising consistent communications performance for C4ISR1 systems even in dynamic and hostile battlefield environments. As such, the vision of Network-Centric Operations becomes feasible. However, enabling adaptation and learning in CRs may require both storing a vast volume of data and processing it fast. Because a CR usually has limited computing and storage capacity determined by its size and battery, it may not be able to achieve its full capability. The cloud2 can provide its computing and storage utility for CRs to overcome such challenges. On the other hand, the cloud can also store and process enormous amounts of data needed by C4ISR systems. However, today's wireless technologies have difficulty moving various types of data reliably and promptly in the battlefields. CR networks promise reliable and timely data communications for accessing the cloud. Overall, connecting CRs and the cloud overcomes the performance bottlenecks of each. This paper explores opportunities of this confluence and describes our prototype system.

Islamic banking is one of the new growing financial streams that emerged in the Islamic world in the seventh decade of last century. Emerging from pioneering counties such as Malaysia, the Islamic financial concept had to adjust many of the strategies in order to comply with the Islamic law. The heart of the Islamic law’s financial instructions is the prohibition of interest and the sharing of profit and losses between the capital provider and the borrower. However, such a concept imposes many implications on the risk management of the financial institute adopting the Islamic banking concept. While traditional risk types, including credit, market, liquidity and operational risks, apply in Islamic banking, the sources of risk and mitigation strategies differ in comparison with conventional banking. Furthermore, there are unique risk types that accompany Islamic banking such as rate of return risk, equity investment risk, Sharia non-compliance risk, and displaced commercial risk. On the practical side, a case study of risk management in Libyan banks adopting Islamic banking principles isevaluated as a diagnostic research. The outcomes of the study show immaturity in the concept of risk management in the country affected by many non-financial factors. Therefore, the researcher provides his recommendation in order to empower development of the risk management concept in Libyan Banks

Summarization: Wide range of digital systems from wireless devices to multi-media terminals are characterized by their multi-mode behavior. Many of these systems are deployed in high-radiation environments [5]. SRAM-based FPGAs are popular platforms to implement multi-mode systems, because of their high performance and reconfigurability. However, high susceptibility of FPGAs toward Soft Errors makes them less-than-reliable platforms. To overcome the reliability issue, various redundancy techniques have been proposed. These techniques exhibit different design and reliability characteristics. Considering the combined effect of design decisions and reliability techniques on system characteristics a coherent strategy should be devised to meet system requirements and constraints. In this work we propose a method to explore the design space for implementing a reliable multi-mode system. As we will show, different selections of design parameters and redundancy techniques generate a range of solutions which trade-off total area, reconfiguration overhead and reliability of the system. The choice of a specific solution remains a decision made by the system-designer. Παρουσιάστηκε στο: IEEE International Design and Test Workshop (IDT), in conjunction with the IEEE International Conference on Electronics, Circuits and Systems