NeuroSim: A Circuit-Level Macro Model for Benchmarking Neuro-Inspired Architectures in Online Learning
NeuroSim: A Circuit-Level Macro Model for Benchmarking Neuro-Inspired Architectures in Online Learning
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DOI：
10.1109/tcad.2018.2789723
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发表时间：
2018-12-01
影响因子：
2.9
通讯作者：
Yu, Shimeng
中科院分区：
文献类型：
--
作者：
Chen, Pai-Yu;Peng, Xiaochen;Yu, Shimeng
已经提出了基于突触内存阵列的神经启发的体系结构，用于在机器/深度学习算法中加速加权和权重更新的芯片加速。在本文中，我们开发了Neurosim，这是一种电路级宏模型，估算了该区域，潜伏期，动态能量和泄漏功率，以促进具有主流和新兴设备技术的神经启发体系结构的设计空间探索。 Neurosim在电路和设备级别提供灵活的界面和各种设计选项。因此，神经网络（NNS）可以将Neurosim用作提供电路级性能评估的支持工具。使用Neurosim，可以使用层次组织构建一个集成框架，从设备级别（突触设备属性）到电路级别（阵列架构），然后再到算法级别（NN拓扑），从而启用指导 - 准确性评估的学习准确性，以此作为学习准确性的评估以及在线学习运行时的电路级性能指标。使用多层perceptron作为案例研究算法，我们调查了“模拟”新兴的非挥发性内存（ENKM）的“非理想”设备属性的影响，并基准了SRAM，数字和类似物Envm学习在线学习之间的权衡和离线分类。
Neuro-inspired architectures based on synaptic memory arrays have been proposed for on-chip acceleration of weighted sum and weight update in machine/deep learning algorithms. In this paper, we developed NeuroSim, a circuit-level macro model that estimates the area, latency, dynamic energy, and leakage power to facilitate the design space exploration of neuro-inspired architectures with mainstream and emerging device technologies. NeuroSim provides flexible interface and a wide variety of design options at the circuit and device level. Therefore, NeuroSim can be used by neural networks (NNs) as a supporting tool to provide circuit-level performance evaluation. With NeuroSim, an integrated framework can be built with hierarchical organization from the device level (synaptic device properties) to the circuit level (array architectures) and then to the algorithm level (NN topology), enabling instruction-accurate evaluation on the learning accuracy as well as the circuit-level performance metrics at the run-time of online learning. Using multilayer perceptron as a case-study algorithm, we investigated the impact of the "analog" emerging nonvolatile memory (eNVM)'s "nonideal" device properties and benchmarked the tradeoffs between SRAM, digital, and analog eNVM-based architectures for online learning and offline classification.