Comprehensive Analytic Performance Assessment and K-means based Multicast Routing Algorithm and Architecture for 3D-NoC of Spiking Neurons

Abstract

Spiking neural networks (SNNs) are artificial neural network models that more closely mimic biological neural networks. In addition to neuronal and synaptic state, SNNs incorporate the variant time scale into their computational model. Since each neuron in these networks is connected to thousands of others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, very low communication latency is also needed. The 2D-NoC was used as a solution to provide a scalable interconnection fabric in large-scale parallel SNN systems. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. The combination of these two emerging technologies provides a new horizon for IC designs to satisfy the high requirements of low power and small footprint in emerging AI applications. In this work, we first present a comprehensive analytical model to analyze the performance of 3D mesh NoC over variants of different SNN topologies and communications protocols. Second, we present an architecture and a low-latency spike routing algorithm, named shortest path K-means based multicast (SP-KMCR), for three-dimensional NoC of spiking neurons (3DNoC-SNN). The proposed system was validated based on an RTL-level implementation, while area/power analysis was performed using 45nm CMOS technology.

Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System

“…Hardware implementations of SNNs (neuromorphic) also have the advantage of computational speedup over software simulations and can take full advantage of their inherent parallelism. Specialized hardware architectures with multiple neuro-cores could exploit the parallelism inherent within neural networks to provide high processing speeds with low power, which make SNNs suitable for embedded neuromorphic devices and control applications (Vu et al, 2019). In general, the neuromorphic hardware systems consist of multiple nodes (or clusters of neurons) connected via an on-chip communication infrastructure (Akopyan et al, 2015;Ogbodo et al, 2020).…”

“…However, the "mimicked" network is generally a 3D structure. Therefore, mapping a 3D structure onto 2D circuits may result in either multiple long wires between layers or congestion points (Vu et al, 2019;Dang et al, 2020b;Ikechukwu et al, 2021).…”

“…Here, the anatomical architecture of Spaun indicates large brain structures, and their connectivity is illustrated with thick dark-yellow lines for communication between elements of the cortex. In contrast, thin lines show connectivity between Basal Ganglia and the cortex (Eliasmith et al, 2012;Vu et al, 2019). However, despite bringing several benefits of lower power, smaller footprints, and low latency, the integration of a neuromorphic system into 3D-ICs was not well investigated.…”

Real-time Hand-Gesture Recognition based on Deep Neural Network

“…A Neural Network (NN) is a type of machine learning method that mimics human neurons' activity. Neural network technology is used for the automatic driving of cars, games, monitoring, and voice recognition [1][2][3][4][5][6][7]. Among them, in the gesture recognition system using neural networks, its application using various tools and algorithms, and its importance are attracting attention in robot control [8].…”

AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform

“…We then establish a novel communication mechanism between the aggregator and each EV node, in which an AI system based on reconfigurable hardware (FPGA) is used to predict the amount of available electricity an EV could supply when idle to mitigate storage during peak load. The reconfigurable AI system, with high-speed computation and low-power consumption, can be packaged into an extended electronic control unit (ECU) connected to the controller area network (CAN) bus of a car [47], [48], as shown in Fig. 2.…”