NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems

• URL: http://arxiv.org/abs/2106.12125v1
• Date: Wed, 23 Jun 2021 02:27:00 GMT
• Title: NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems
• Authors: Shubham Negi, Indranil Chakraborty, Aayush Ankit, Kaushik Roy
• Abstract summary: In-Memory Computing (IMC) hardware using Memristive Crossbar Arrays (MCAs) are gaining popularity to accelerate Deep Neural Networks (DNNs) We propose NAX -- an efficient neural architecture search engine that co-designs neural network and IMC based hardware architecture.
• Score: 7.481928921197249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-Memory Computing (IMC) hardware using Memristive Crossbar Arrays (MCAs) are gaining popularity to accelerate Deep Neural Networks (DNNs) since it alleviates the "memory wall" problem associated with von-Neumann architecture. The hardware efficiency (energy, latency and area) as well as application accuracy (considering device and circuit non-idealities) of DNNs mapped to such hardware are co-dependent on network parameters, such as kernel size, depth etc. and hardware architecture parameters such as crossbar size. However, co-optimization of both network and hardware parameters presents a challenging search space comprising of different kernel sizes mapped to varying crossbar sizes. To that effect, we propose NAX -- an efficient neural architecture search engine that co-designs neural network and IMC based hardware architecture. NAX explores the aforementioned search space to determine kernel and corresponding crossbar sizes for each DNN layer to achieve optimal tradeoffs between hardware efficiency and application accuracy. Our results from NAX show that the networks have heterogeneous crossbar sizes across different network layers, and achieves optimal hardware efficiency and accuracy considering the non-idealities in crossbars. On CIFAR-10 and Tiny ImageNet, our models achieve 0.8%, 0.2% higher accuracy, and 17%, 4% lower EDAP (energy-delay-area product) compared to a baseline ResNet-20 and ResNet-18 models, respectively.

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