Digital neuromorphic chips for deep learning inference: a comprehensive study

Hamed F. Langroudi; Tej Pandit; Mark Indovina; Dhireesha Kudithipudi

doi:10.1117/12.2529407

6 September 2019 Digital neuromorphic chips for deep learning inference: a comprehensive study

Hamed F. Langroudi, Tej Pandit, Mark Indovina, Dhireesha Kudithipudi

Proceedings Volume 11139, Applications of Machine Learning; 111390C (2019) https://doi.org/10.1117/12.2529407
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

Over the past few years, deep neural networks have achieved state-of-the-art accuracy in a broad spectrum of applications. However, implementing deep networks in general purpose architectures is a challenging task as they require high computational resources and massive memory bandwidth. Recently, several digital neuromorphic chips have been proposed to address these issues. In this paper, we explore sixteen prominent rate based digital neuromorphic chip architectures, optimized primarily for inference. Specific focus is on: What is the motivation to design digital neuromorphic chips? Which optimizations play a key role in improving their performance? What are the main research trends in current generation chips?

Citation Download Citation

Hamed F. Langroudi, Tej Pandit, Mark Indovina, and Dhireesha Kudithipudi "Digital neuromorphic chips for deep learning inference: a comprehensive study", Proc. SPIE 11139, Applications of Machine Learning, 111390C (6 September 2019); https://doi.org/10.1117/12.2529407

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