Table-Lookup MAC: Scalable Processing of Quantised Neural Networks in FPGA Soft Logic-Reference-Cited by-同舟云学术

Table-Lookup MAC: Scalable Processing of Quantised Neural Networks in FPGA Soft Logic

Published:2024-04 Issue: Volume:86 Page:235-245
ISSN:
Container-title:Proceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays
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Author:

Gerlinghoff Daniel¹^ORCID,Choong Benjamin Chen Ming¹^ORCID,Goh Rick Siow Mong¹^ORCID,Wong Weng-Fai²^ORCID,Luo Tao¹^ORCID

Affiliation:

1. Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore

2. National University of Singapore, Singapore, Singapore

Funder

Agency for Science, Technology and Research

National Research Foundation Singapore, Quantum Engineering Programme 2.0 (National Quantum Computing Hub)

Singapore Government?s Research, Innovation and Enterprise 2020 Plan (Advanced Manufacturing and Engineering domain)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626202.3637576

Reference35 articles.

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2. Marta Andronic and George A Constantinides. 2023. PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference. arXiv preprint arXiv:2309.02334.

3. Aman Arora, Tanmay Anand, Aatman Borda, Rishabh Sehgal, Bagus Hanindhito, Jaydeep Kulkarni, and Lizy K John. 2022. CoMeFa: Compute-in-Memory Blocks for FPGAs. In 2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 1--9.

4. Aman Arora, Atharva Bhamburkar, Aatman Borda, Tanmay Anand, Rishabh Sehgal, Bagus Hanindhito, Pierre-Emmanuel Gaillardon, Jaydeep Kulkarni, and Lizy K John. 2023. CoMeFa: Deploying Compute-in-Memory on FPGAs for Deep Learning Acceleration. ACM Transactions on Reconfigurable Technology and Systems (2023).

5. Redundant feature pruning for accelerated inference in deep neural networks