Abu Sebastian, Irem Boybat, Martino Dazzi, Iason Giannopoulos, Varaprasad Jonnalagadda, Vinay Joshi, Geethan Karunaratne, Benedikt Kersting, Riduan Khaddam-Aljameh, S. R. Nandakumar, Anastasios Petropoulos, Christophe Piveteau, Theodore Antonakopoulos, Bipin Rajendran, Manuel Le Gallo, Evangelos Eleftheriou:

Computational memory-based inference and training of deep neural networks

2019 Symposia on VLSI Technology and Circuits, Kyoto, Japan, June 9-14, 2019.

Abstract: In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.

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