Mohammed Al-Sanabani
ORCID:
0009-0002-5801-2090
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About
Contact & Profiles
A5065623938
Research Areas
- Open Source Software Innovations
- Low-power high-performance VLSI design
- Parallel Computing and Optimization Techniques
- Stochastic Gradient Optimization Techniques
Meta (United States)
2023
Amin Firoozshahian
Joel Coburn
Roman Levenstein
Rakesh Nattoji
Ashwin Kamath
and 51
more
Olivia Wu
G.P. Grewal
Harish Aepala
Bhasker Jakka
Bob Dreyer
Adam Hutchin
Utku Diril
Krishnakumar Nair
Ehsan K. Aredestani
Martin Schatz
Yuchen Hao
Rakesh Komuravelli
Kunming Ho
Sameer Abu Asal
Joe Shajrawi
Kevin M. Quinn
Nagesh Sreedhara
Pankaj Kansal
W.-H. Wei
Dheepak Jayaraman
Linda Cheng
Pritam Chopda
Eric K. Wang
Ajay Bikumandla
Arun Karthik Sengottuvel
Krishna Thottempudi
Ashwin Narasimha
Brian Dodds
C. Gao
J. Zhang
Mohammed Al-Sanabani
Ana Zehtabioskuie
Jordan Fix
Hangchen Yu
Richard Li
Kaustubh Gondkar
Jack G. Montgomery
Mike Tsai
Saritha Dwarakapuram
S. Desai
Nili Avidan
P. V. Ramani
Karthik Narayanan
Ajit Mathews
S. Gopal
Maxim Naumov
Vijay Rao
Krishna Noru
Harikrishna Reddy
Prahlad Venkatapuram
Alexis Bjorlin
Meta has traditionally relied on using CPU-based servers for running inference workloads, specifically Deep Learning Recommendation Models (DLRM), but the increasing compute and memory requirements of these models have pushed company towards specialized solutions such as GPUs or other hardware accelerators. This paper describes company's effort in constructing its first silicon designed recommendation systems; it accelerator architecture platform design, software stack enabling optimizing...
10.1145/3579371.3589348
article
EN
2023-06-16
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