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Towards Resilient Deployment of In-Memory Neural Networks with High Throughput
AI/ML System Design
DescriptionResistive computing systems (RCSs) promise exascale computing capabilities to inference engines for deep learning. In this paper, we propose a framework for resilient deployment of high throughput CNNs to RCSs. The framework is based on integrating a data layout organization step and a distribution guided training step into the flow for mapping CNNs to RCSs. The experimental results demonstrate that the proposed techniques expand the average solution space for data layout organization with 1.4* 10^14X. This translates into that CNNs with high throughput can be deployed onto RCS with up to 10% defects and still attain high classification accuracy.