Work

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Year: 2025

Type: report

Abstract: Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment.

DOI: 10.2172/3010166

Authors: Jason Weitz, UC, San Diego, Dmitri Demler, Benjamin Hawks, Nhan Tran, Javier Duarte

Institutions: University of California San Diego, Fermi National Accelerator Laboratory

Language: English

Topic: Embedded Systems Design Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: National Science Foundation, U.S. Department of Energy, China Scholarship Council, Office of Science, Advanced Scientific Computing Research, High Energy Physics

Awards: TRAINEESHIP IN COMPUTATIONAL HIGH ENERGY PHYSICS, Collaborative Proposal: Geodynamic Tomography: A New Approach to Studies of Mantle Upwelling and its Application to the Lau Basin and The East Pacific Rise, G2926102538, G3270322552, G4404904460, G498139845, G5211103837, G5574724152, G5616713406, G585908603, G6471802762, HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery, G716412294, REAL-TIME ARTIFICIAL INTELLIGENCE FOR PARTICLE RECONSTRUCTION AND HIGGS PHYSICS, REAL-TIME ARTIFICIAL INTELLIGENCE FOR PARTICLE RECONSTRUCTION AND HIGGS PHYSICS, G8623004658, G86560745, G969889393

OpenAlex ID: W7117136363

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Cited by: 1

Related to: 10