Year: 2024
Type: conference-paper
Abstract: Large language models (LLMs) are foundational to the advancement of state-of-the-art natural language processing (NLP) and computer vision applications. However, their intricate architectures and the complexity of their underlying neural networks present significant challenges for efficient acceleration on conventional electronic platforms. Silicon photonics offers a compelling alternative. In this paper, we describe our recent efforts on developing a novel hardware accelerator that leverages silicon photonics to accelerate transformer neural networks integral to LLMs. Our evaluation demonstrates that the proposed accelerator delivers up to 14× higher throughput and 8× greater energy efficiency compared to leading-edge LLM hardware accelerators, including CPUs, GPUs, and TPUs.
Authors: Mahdi Nikdast, Salma Afifi, Sudeep Pasricha
Institution: Colorado State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W4409282658