NeuroSim: A Circuit-Level Macro Model for Benchmarking Neuro-Inspired Architectures in Online Learning

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 37; no. 12; pp. 3067 - 3080
• Main Authors: Chen, Pai-Yu, Peng, Xiaochen, Yu, Shimeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Artificial neural networks; Circuit design; Computer architecture; Distance learning; Emerging nonvolatile memory (eNVM); Integrated circuit modeling; Machine learning; Microprocessors; Multilayer perceptrons; neural network (NN); Neural networks; neuromorphic computing; Neuromorphics; offline classification; online learning; Performance evaluation; Performance measurement; Static random access memory; synaptic devices; Weight
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2018.2789723

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.