Summary of Polylut-add: Fpga-based Lut Inference with Wide Inputs, by Binglei Lou et al.
PolyLUT-Add: FPGA-based LUT Inference with Wide Inputs
by Binglei Lou, Richard Rademacher, David Boland, Philip H.W. Leong
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces PolyLUT-Add, a technique that enhances neuron connectivity by combining PolyLUT sub-neurons via addition to improve accuracy on FPGAs. The approach maximizes the advantages of deploying deep neural networks at the edge by reducing LUT resource usage and latency. The authors describe a novel architecture to enhance scalability and evaluate their implementation on MNIST, Jet Substructure classification, and Network Intrusion Detection benchmark datasets. The results show that PolyLUT-Add achieves a LUT reduction of 2.0-13.9 times with a decrease in latency of 1.2-1.6 times for similar accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computers at the edge faster and more efficient by using special chips called FPGAs (Field-Programmable Gate Arrays). The authors developed a new way to connect neural networks, which are like special kinds of computer programs that can recognize patterns. This new way helps reduce the amount of memory needed on these special chips, making them even faster and more useful for tasks like image recognition and security monitoring. |
Keywords
* Artificial intelligence * Classification
