M
M. Le Gallo
Researcher at IBM
Publications - 10
Citations - 669
M. Le Gallo is an academic researcher from IBM. The author has contributed to research in topics: Deep learning & In-Memory Processing. The author has an hindex of 7, co-authored 10 publications receiving 248 citations.
Papers
More filters
Journal ArticleDOI
Parallel convolutional processing using an integrated photonic tensor core.
Johannes Feldmann,Nathan Youngblood,Nathan Youngblood,Maxim Karpov,Helge Gehring,Xuan Li,Maik Stappers,M. Le Gallo,Xin Fu,Anton Lukashchuk,Arslan S. Raja,Junqiu Liu,C.D. Wright,Abu Sebastian,Tobias J. Kippenberg,Wolfram H. P. Pernice,Harish Bhaskaran +16 more
TL;DR: In this paper, the authors demonstrate a computationally specific integrated photonic hardware accelerator (tensor core) that is capable of operating at speeds of trillions of multiply-accumulate operations per second.
Proceedings ArticleDOI
8-bit Precision In-Memory Multiplication with Projected Phase-Change Memory
Iason Giannopoulos,Abu Sebastian,M. Le Gallo,V. P. Jonnalagadda,Marilyne Sousa,M.N. Boon,Evangelos Eleftheriou +6 more
TL;DR: It is demonstrated that the so-called projected phase-change memory (Proj-PCM) devices can achieve 8-bit precision while performing scalar multiplication and is found to be remarkably immune to conductance variations arising from structural relaxation, $1/f$ noise and temperature variations.
Proceedings ArticleDOI
HERMES Core – A 14nm CMOS and PCM-based In-Memory Compute Core using an array of 300ps/LSB Linearized CCO-based ADCs and local digital processing
Riduan Khaddam-Aljameh,Milos Stanisavljevic,J. Fornt Mas,Geethan Karunaratne,Matthias Braendli,Fei Liu,A. Singh,S. M. Muller,U. Egger,Anastasios Petropoulos,Theodore Antonakopoulos,Kevin W. Brew,S. Choi,Ok Injo,Fee Li Lie,Nicole Saulnier,Victor Chan,Ishtiaq Ahsan,Vijay Narayanan,S. R. Nandakumar,M. Le Gallo,Pier Andrea Francese,Abu Sebastian,Evangelos Eleftheriou +23 more
TL;DR: In this article, a 256×256 in-memory compute (IMC) core designed and fabricated in 14nm CMOS with backend-integrated multi-level phase change memory (PCM).
Proceedings ArticleDOI
Compressed sensing recovery using computational memory
TL;DR: This work proposes a new method for fast and robust compressed sensing recovery of sparse signals using CM that achieves a potential O(N)-fold complexity reduction compared with a standard software approach.
Journal ArticleDOI
Deep learning acceleration based on in-memory computing
Evangelos S. Eleftheriou,M. Le Gallo,S. R. Nandakumar,Christophe Piveteau,Irem Boybat,Vinay Joshi,Riduan Khaddam-Aljameh,Martino Dazzi,Iason Giannopoulos,Geethan Karunaratne,Benedikt Kersting,Milos Stanisavljevic,V. P. Jonnalagadda,Nikolas Ioannou,Kornilios Kourtis,P. A. Francese,Abu Sebastian +16 more
TL;DR: This article focuses on mixed-precision deep learning training with in-memory computing, and shows how the precision of in- memory computing can be further improved through architectural and device-level innovations.