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Computer Science > Hardware Architecture

arXiv:2409.03384 (cs)
[Submitted on 5 Sep 2024]

Title:Hardware Acceleration of LLMs: A comprehensive survey and
comparison

Authors:Nikoletta Koilia, Christoforos Kachris
View a PDF of the paper titled Hardware Acceleration of LLMs: A
comprehensive survey and comparison, by Nikoletta Koilia and
Christoforos Kachris
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    Abstract:Large Language Models (LLMs) have emerged as powerful
    tools for natural language processing tasks, revolutionizing the
    field with their ability to understand and generate human-like
    text. In this paper, we present a comprehensive survey of the
    several research efforts that have been presented for the
    acceleration of transformer networks for Large Language Models
    using hardware accelerators.
    The survey presents the frameworks that have been proposed and
    then performs a qualitative and quantitative comparison regarding
    the technology, the processing platform (FPGA, ASIC, In-Memory,
    GPU), the speedup, the energy efficiency, the performance (GOPs),
    and the energy efficiency (GOPs/W) of each framework. The main
    challenge in comparison is that every proposed scheme is
    implemented on a different process technology making hard a fair
    comparison. The main contribution of this paper is that we
    extrapolate the results of the performance and the energy
    efficiency on the same technology to make a fair comparison; one
    theoretical and one more practical. We implement part of the LLMs
    on several FPGA chips to extrapolate the results to the same
    process technology and then we make a fair comparison of the
    performance.

Comments: this https URL
Subjects: Hardware Architecture (cs.AR); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2409.03384 [cs.AR]
          (or arXiv:2409.03384v1 [cs.AR] for this version)
          https://doi.org/10.48550/arXiv.2409.03384
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Christoforos Kachris [view email]
[v1] Thu, 5 Sep 2024 09:43:25 UTC (1,209 KB)
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