Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices - Spoon, Katie; Tsai, Hsinyu; Chen, An; Rasch, Malte J.; Ambrogio, Stefano; Mackin, Charles; Fasoli, Andrea; Friz, Alexander M.; Narayanan, Pritish; Stanisavljevic, Milos; Burr, Geoffrey W. (Frontiers in Computational Neuroscience, 2021)
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.