Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Conference · Wed Dec 17 04:00:00 UTC 2025 · No journal information

OSTI ID:3010171

Weitz, Jason ^[1]; Demler, Dmitri ^[1]; Hawks, Benjamin ^[2]; Tran, Nhan ^[2]; Duarte, Javier ^[1]

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1. UC, San Diego
2. Fermilab

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. SNAC-Pack combines Neural Architecture Codesign's multi-stage search capabilities with the Resource Utilization and Latency Estimator, enabling multi-objective optimization across accuracy, FPGA resource utilization, and latency without requiring time-intensive synthesis for each candidate model. We demonstrate SNAC-Pack on a high energy physics jet classification task, achieving 63.84% accuracy with resource estimation. When synthesized on a Xilinx Virtex UltraScale+ VU13P FPGA, the SNAC-Pack model matches baseline accuracy while maintaining comparable resource utilization to models optimized using traditional BOPs metrics. This work demonstrates the potential of hardware-aware neural architecture search for resource-constrained deployments and provides an open-source framework for automating the design of efficient FPGA-accelerated models.

• Research Organization: UC, San Diego; Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: US Department of Energy
• DOE Contract Number: 89243024CSC000002
• OSTI ID: 3010171
• Report Number(s): FERMILAB-CONF-25-0834-CSAID; oai:inspirehep.net:3093886; arXiv:2512.15998
• Resource Type: Conference paper
• Conference Information: Journal Name: No journal information
• Country of Publication: United States
• Language: English