Choose your tools carefully: a comparative evaluation of deterministic vs. stochastic and binary vs. analog neuron models for implementing emerging computing paradigms

Frontiers in Nanotechnology

Nanotechnology

Md Golam Morshed¹  Avik W. Ghosh²  Samiran Ganguly³ 

[1] Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States;Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States;Department of Physics, University of Virginia, Charlottesville, VA, United States;Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, United States;

关键词: neuromorphic computing;    analog neuron;    binary neuron;    analog stochastic neuron;    binary stochastic neuron;    reservoir computing;   

DOI  :  10.3389/fnano.2023.1146852

 received in 2023-01-18, accepted in 2023-04-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

Neuromorphic computing, commonly understood as a computing approach built upon neurons, synapses, and their dynamics, as opposed to Boolean gates, is gaining large mindshare due to its direct application in solving current and future computing technological problems, such as smart sensing, smart devices, self-hosted and self-contained devices, artificial intelligence (AI) applications, etc. In a largely software-defined implementation of neuromorphic computing, it is possible to throw enormous computational power or optimize models and networks depending on the specific nature of the computational tasks. However, a hardware-based approach needs the identification of well-suited neuronal and synaptic models to obtain high functional and energy efficiency, which is a prime concern in size, weight, and power (SWaP) constrained environments. In this work, we perform a study on the characteristics of hardware neuron models (namely, inference errors, generalizability and robustness, practical implementability, and memory capacity) that have been proposed and demonstrated using a plethora of emerging nano-materials technology-based physical devices, to quantify the performance of such neurons on certain classes of problems that are of great importance in real-time signal processing like tasks in the context of reservoir computing. We find that the answer on which neuron to use for what applications depends on the particulars of the application requirements and constraints themselves, i.e., we need not only a hammer but all sorts of tools in our tool chest for high efficiency and quality neuromorphic computing.

【 授权许可】

Unknown   
Copyright © 2023 Morshed, Ganguly and Ghosh.