DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip Training

• URL: http://arxiv.org/abs/2003.06471v1
• Date: Fri, 13 Mar 2020 20:20:42 GMT
• Title: DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip Training
• Authors: Xiaochen Peng, Shanshi Huang, Hongwu Jiang, Anni Lu, Shimeng Yu
• Abstract summary: NeuroSim is an integrated framework to benchmark compute-in-memory (CIM) accelerators for deep neural networks. A python wrapper is developed to interface NeuroSim with a popular machine learning platform: Pytorch.
• Score: 4.555081317066413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: DNN+NeuroSim is an integrated framework to benchmark compute-in-memory (CIM) accelerators for deep neural networks, with hierarchical design options from device-level, to circuit-level and up to algorithm-level. A python wrapper is developed to interface NeuroSim with a popular machine learning platform: Pytorch, to support flexible network structures. The framework provides automatic algorithm-to-hardware mapping, and evaluates chip-level area, energy efficiency and throughput for training or inference, as well as training/inference accuracy with hardware constraints. Our prior work (DNN+NeuroSim V1.1) was developed to estimate the impact of reliability in synaptic devices, and analog-to-digital converter (ADC) quantization loss on the accuracy and hardware performance of inference engines. In this work, we further investigated the impact of the analog emerging non-volatile memory non-ideal device properties for on-chip training. By introducing the nonlinearity, asymmetry, device-to-device and cycle-to-cycle variation of weight update into the python wrapper, and peripheral circuits for error/weight gradient computation in NeuroSim core, we benchmarked CIM accelerators based on state-of-the-art SRAM and eNVM devices for VGG-8 on CIFAR-10 dataset, revealing the crucial specs of synaptic devices for on-chip training. The proposed DNN+NeuroSim V2.0 framework is available on GitHub.

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