Random Matrix Methods for Wireless Communications: Perspectives

Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. It has been postulated that these networks are more efficient for realizing cognitive computing systems compared to second generation networks that are widely used in machine learning algorithms today. In this paper, we review the learning algorithms, hardware demonstrations and potential applications of SNN based learning systems.

Spiking neural networks — Algorithms, hardware implementations and applications

BIO-INSPIRED LEARNING AND HARDWARE ACCELERATION WITH EMERGING MEMORIES by Shruti R. Kulkarni Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called action potentials. Motivated by these facts, the Spiking Neural Networks (SNNs), which are a bio-plausible version of neural networks have been proposed as an alternative computing paradigm where the timing of spikes generated by artificial neurons is central to its learning and inference capabilities. This dissertation demonstrates the computational power of the SNNs using conventional CMOS and emerging nanoscale hardware platforms. The first half of this dissertation presents an SNN architecture which is trained using a supervised spike-based learning algorithm for the handwritten digit classification problem. This network achieves an accuracy of 98.17% on the MNIST test data-set, with about 4× fewer parameters compared to the state-of-the-art neural networks achieving over 99% accuracy. In addition, a scheme for parallelizing and speeding up the SNN simulation on a GPU platform is presented. The second half of this dissertation presents an optimal hardware design for accelerating SNN inference and training with SRAM (Static Random Access Memory) and nanoscale non-volatile memory (NVM) crossbar arrays. Three prominent NVM devices are studied for realizing hardware accelerators for SNNs: Phase Change Memory (PCM), Spin Transfer Torque RAM (STT-RAM) and Resistive RAM (RRAM). The analysis shows that a spike-based inference engine with crossbar arrays of STT-RAM bit-cells is 2× and 5× more efficient compared to PCM and RRAM memories, respectively. Furthermore, the STT-RAM design has nearly 6× higher throughput per unit Watt per unit area than that of an equivalent SRAM-based (Static Random Access Memory) design. A hardware accelerator with on-chip learning on an STT-RAM memory array is also designed, requiring 16 bits of floating-point synaptic weight precision to reach the baseline SNN algorithmic performance on the MNIST dataset. The complete design with STT-RAM crossbar array achieves nearly 20× higher throughput per unit Watt per unit mm than an equivalent design with SRAM memory. In summary, this work demonstrates the potential of spike-based neuromorphic computing algorithms and its efficient realization in hardware based on conventional CMOS as well as emerging technologies. The schemes presented here can be further extended to design spike-based systems that can be ubiquitously deployed for energy and memory constrained edge computing applications. BIO-INSPIRED LEARNING AND HARDWARE ACCELERATION WITH EMERGING MEMORIES by Shruti R. Kulkarni A Dissertation Submitted to the Faculty of New Jersey Institute of Technology – Newark in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Electrical Engineering Helen and John C. Hartmann Department of Electrical and Computer Engineering

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Bio-inspired learning and hardware acceleration with emerging memories

We demonstrate a Spiking Neural Network (SNN) driven autonomous navigation system implemented on a robot. The neural architecture is inspired by those in nematode Caenorhabditis elegans used for thermotaxis, the behavior of tracking thermal isotherms. Our network uses light intensity as the sensor input, instead of temperature in the worm. The network is able to detect the gradations in sensor-input based on local information, and to make decisions in real time. This enables the robot to do a random search and to track specific intensity regions.

Live demonstration: Spiking neural circuit based navigation inspired by C. elegans thermotaxis

In a coherent time-varying MIMO multiple access channel (MAC), the ergodic sum-capacity can be achieved by suitable power control, which adapts the transmit covariance matrices based on the available channel state information (CSI). We consider a MIMO-MAC model where each transmitter is only aware of its own fading realization. For this distributed MAC, we propose decentralized power control schemes. First, some key structural properties on the optimal power control laws are derived, followed by an upper bound on the sum-capacity. Numerical results show that the proposed power control schemes closely characterize the sum-capacity in several regimes of interest.

Chirag C Shetty

Papers

Live demonstration: Spiking neural circuit based navigation inspired by C. elegans thermotaxis

On Decentralized Power Control for Ergodic MIMO Multiple Access Channels