Choose your tools carefully: a comparative evaluation of deterministic vs. stochastic and binary vs. analog neuron models for implementing emerging computing paradigms
Choose your tools carefully: a comparative evaluation of deterministic vs. stochastic and binary vs. analog neuron models for implementing emerging computing paradigms
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DOI：
10.3389/fnano.2023.1146852
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发表时间：
2023-02
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影响因子：
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通讯作者：
Md Golam Morshed;S. Ganguly;Avik W. Ghosh
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作者：
Md Golam Morshed;S. Ganguly;Avik W. Ghosh
神经形态计算，通常被理解为建立在神经元，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，在神经形态计算的主要软件定义的实现中，可以根据计算任务的特定性质来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合适的神经元和突触模型，以获得高的功能和能量效率，这是尺寸，重量和功率（SWaP）受限环境中的主要问题。在这项工作中，我们进行了研究的特点，硬件神经元模型（即推理错误、概括性和鲁棒性、实际可实施性和记忆容量），这些都是使用大量新兴的基于纳米材料技术的物理设备提出和证明的，量化这些神经元在某些类别的问题上的表现，这些问题在真实的中非常重要-时间信号处理类似于油藏计算环境中的任务。我们发现，关于哪个神经元用于什么应用的答案取决于应用要求和约束本身的细节，即，我们不仅需要一把锤子，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。
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.