Conference paper Open Access

Computational memory-based inference and training of deep neural networks

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

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.

Files (760.8 kB)
Name Size
Y2019_sebastian_VLSI.pdf
md5:edd12be82dd8239a504f29cdeee3a0b1
760.8 kB Download
6
6
views
downloads
Views 6
Downloads 6
Data volume 4.6 MB
Unique views 5
Unique downloads 5

Share

Cite as