Computational memory-based inference and training of deep neural networks
Abu Sebastian, Irem Boybat, Martino Dazzi, Iason Giannopoulos, Vaishnav Teja Jonnalagadda, Vinay Joshi, Geethan Karunaratne, Benedikt Kersting, Riduan Khaddam-Aljameh, S. R. Nandakumar, Αναστάσιος Πετρόπουλος, Christophe Piveteau, Theodore A. Antonakopoulos, Bipin Rajendran, Manuel Le Gallo, Evangelos S. Eleftheriou · 2019
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.