仔细选择你的工具:对实现新兴计算范式的确定性与随机性、二进制与模拟神经元模型的比较评估

IF 4.1 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Md Golam Morshed, S. Ganguly, Avik W. Ghosh
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引用次数: 0

摘要

神经形态计算,通常被理解为一种建立在神经元、突触及其动力学基础上的计算方法,而不是布尔门,由于其直接应用于解决当前和未来的计算技术问题,如智能传感、智能设备、自托管和自包含设备、人工智能(AI)应用等,正获得越来越多的关注。在很大程度上由软件定义的神经形态计算实现中,可以根据计算任务的具体性质投入巨大的计算能力或优化模型和网络。然而,基于硬件的方法需要识别非常适合的神经元和突触模型,以获得高功能和能量效率,这是尺寸,重量和功率(SWaP)受限环境中的主要关注点。在这项工作中,我们对硬件神经元模型的特征进行了研究(即,推理误差,可泛化性和鲁棒性,实际可实现性和内存容量),这些模型已经被提出并使用大量新兴的基于纳米材料技术的物理设备进行了演示,以量化这些神经元在某些类别的问题上的性能,这些问题在实时信号处理中非常重要,如水库计算背景下的任务。我们发现,在哪些应用中使用哪个神经元的答案取决于应用需求和约束本身的细节,也就是说,我们不仅需要一个锤子,还需要工具箱中的各种工具来实现高效率和高质量的神经形态计算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Choose your tools carefully: a comparative evaluation of deterministic vs. stochastic and binary vs. analog neuron models for implementing emerging computing paradigms
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.
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来源期刊
Frontiers in Nanotechnology
Frontiers in Nanotechnology Engineering-Electrical and Electronic Engineering
CiteScore
7.10
自引率
0.00%
发文量
96
审稿时长
13 weeks
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