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arXiv · 1912.00106

A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM

Siming Ma·David Brooks·Gu-Yeon Wei
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Abstract

We propose a new algorithm for training neural networks with binary activations and multi-level weights, which enables efficient processing-in-memory circuits with embedded nonvolatile memories (eNVM). Binary activations obviate costly DACs and ADCs. Multi-level weights leverage multi-level eNVM cells. Compared to existing algorithms, our method not only works for feed-forward networks (e.g., fully-connected and convolutional), but also achieves higher accuracy and noise resilience for recurrent networks. In particular, we present an RNN-based trigger-word detection PIM accelerator, with detailed hardware noise models and circuit co-design techniques, and validate our algorithm's high inference accuracy and robustness against a variety of real hardware non-idealities.

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Siming Ma, David Brooks, Gu-Yeon Wei. 2019-11-30. A binary-activation, multi-level weight RNN and training algorithm for ADC-/DAC-free and noise-resilient processing-in-memory inference with eNVM. https://arxiv.org/abs/1912.00106

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