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

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 leak...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2018.2789723