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Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices

PCM-based analog accelerators are a sensible choice for deep learning workloads, even for large natural language processing models like BERT.

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Abstract: 

“Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential of Analog AI accelerators based on Non-Volatile Memory, in particular Phase Change Memory (PCM), for software-equivalent accurate inference of natural language processing applications. We demonstrate a path to software-equivalent accuracy for the GLUE benchmark on BERT (Bidirectional Encoder Representations from Transformers), by combining noise-aware training to combat inherent PCM drift and noise sources, together with reduced-precision digital attention-block computation down to INT6.”

View this technical paper here. Published 07/2021.

Spoon K, Tsai H, Chen A, Rasch MJ, Ambrogio S, Mackin C, Fasoli A, Friz AM, Narayanan P, Stanisavljevic M and Burr GW (2021) Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices. Front. Comput. Neurosci. 15:675741.



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