@inproceedings{f0dd2c2033184e419a97bb9dfff4b3d3,
title = "Computational memory-based inference and training of deep neural networks",
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.",
keywords = "In-memory computing, PCM, deep learning",
author = "A. Sebastian and I. Boybat and M. Dazzi and I. Giannopoulos and V. Jonnalagadda and V. Joshi and G. Karunaratne and B. Kersting and R. Khaddam-Aljameh and Nandakumar, {S. R.} and A. Petropoulos and C. Piveteau and T. Antonakopoulos and B. Rajendran and Gallo, {M. Le} and E. Eleftheriou",
note = "Publisher Copyright: {\textcopyright} 2019 The Japan Society of Applied Physics.; 39th Symposium on VLSI Technology, VLSI Technology 2019 ; Conference date: 09-06-2019 Through 14-06-2019",
year = "2019",
month = jun,
doi = "10.23919/VLSIT.2019.8776518",
language = "English (US)",
series = "Digest of Technical Papers - Symposium on VLSI Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "T168--T169",
booktitle = "2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers",
address = "United States",
}