Analog-AI Hardware Accelerators for Low-Latency Transformer-Based Language Models (Invited)

Analog Non-Volatile Memory-based accelerators offer highthroughput and energy-efficient Multiply-Accumulate operations for the large Fully-Connected layers that dominate Transformer-based Large Language Models (LLMs). We describe recent chip-demo and architectural efforts, quantify the unique benefits of Fully- (rather than Partially-) Weight-Stationary systems, and discuss factors affecting latency of token-processing and generation.

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Publication Details

Published
2025-04-13
DOI
https://doi.org/10.1109/cicc63670.2025.10983594
Citations
1
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
2.48

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article

Analog-AI Hardware Accelerators for Low-Latency Transformer-Based Language Models (Invited)

Kohji Hosokawa, Shilpa Jain, Geoffrey W. Burr, Charles Mackin et al.
1 citations
Topic Modeling
2.48
article

Analog-AI Hardware Accelerators for Low-Latency Transformer-Based Language Models (Invited)

Kohji Hosokawa, Shilpa Jain, Geoffrey W. Burr, Charles Mackin, Atsuya Okazaki, Abu Sebastian, Hadjer Benmeziane, Andrea Fasoli, Jose Luquin, Manuel Le Gallo, Kaoutar El Maghraoui, Yasuteru Kohda, Julian Büchel, Vijay Narayanan, Pritish Narayanan, Masatoshi Ishii, Corey Lammie, Elena Ferro, Alexander Friz, Irem Boybat, Hsinyu Tsai, William A. Simon, Athanasios Vasilopoulos, A. Chen, Timothy Philicelli
article en
1 citations

Abstract

Analog Non-Volatile Memory-based accelerators offer highthroughput and energy-efficient Multiply-Accumulate operations for the large Fully-Connected layers that dominate Transformer-based Large Language Models (LLMs). We describe recent chip-demo and architectural efforts, quantify the unique benefits of Fully- (rather than Partially-) Weight-Stationary systems, and discuss factors affecting latency of token-processing and generation.

IBM Research - Almaden (US), IBM Research - Zurich (CH), IBM Research - Tokyo (JP)
Intercollegiate Center for Management Science
Openalex Percentile: Top 10%
Topic Modeling
2.48
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