TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems

• URL: http://arxiv.org/abs/2002.11151v3
• Date: Fri, 8 Jan 2021 03:54:43 GMT
• Title: TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems
• Authors: Sourjya Roy, Shrihari Sridharan, Shubham Jain, and Anand Raghunathan
• Abstract summary: crossbar-based computations face a major challenge due to a variety of device and circuit-level non-idealities. We propose TxSim, a fast and customizable modeling framework to functionally evaluate DNN training on crossbar-based hardware.
• Score: 3.1887081453726136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Resistive crossbars have attracted significant interest in the design of Deep Neural Network (DNN) accelerators due to their ability to natively execute massively parallel vector-matrix multiplications within dense memory arrays. However, crossbar-based computations face a major challenge due to a variety of device and circuit-level non-idealities, which manifest as errors in the vector-matrix multiplications and eventually degrade DNN accuracy. To address this challenge, there is a need for tools that can model the functional impact of non-idealities on DNN training and inference. Existing efforts towards this goal are either limited to inference, or are too slow to be used for large-scale DNN training. We propose TxSim, a fast and customizable modeling framework to functionally evaluate DNN training on crossbar-based hardware considering the impact of non-idealities. The key features of TxSim that differentiate it from prior efforts are: (i) It comprehensively models non-idealities during all training operations (forward propagation, backward propagation, and weight update) and (ii) it achieves computational efficiency by mapping crossbar evaluations to well-optimized BLAS routines and incorporates speedup techniques to further reduce simulation time with minimal impact on accuracy. TxSim achieves orders-of-magnitude improvement in simulation speed over prior works, and thereby makes it feasible to evaluate training of large-scale DNNs on crossbars. Our experiments using TxSim reveal that the accuracy degradation in DNN training due to non-idealities can be substantial (3%-10%) for large-scale DNNs, underscoring the need for further research in mitigation techniques. We also analyze the impact of various device and circuit-level parameters and the associated non-idealities to provide key insights that can guide the design of crossbar-based DNN training accelerators.

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