Summary of Enhancing Dropout-based Bayesian Neural Networks with Multi-exit on Fpga, by Hao Mark Chen et al.
Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA
by Hao Mark Chen, Liam Castelli, Martin Ferianc, Hongyu Zhou, Shuanglong Liu, Wayne Luk, Hongxiang Fan
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes an algorithm and hardware co-design framework for efficient Bayesian neural networks (BayesNNs) that can be implemented on field-programmable gate arrays (FPGAs). The framework consists of novel multi-exit dropout-based BayesNNs with reduced computational and memory overheads, achieving high accuracy and quality of uncertainty estimation. At the hardware level, a transformation framework is introduced to generate FPGA-based accelerators for the proposed efficient multi-exit BayesNNs. Several optimization techniques are employed to reduce resource consumption and improve overall hardware performance. Comprehensive experiments demonstrate that this approach achieves higher energy efficiency compared to CPU, GPU, and other state-of-the-art hardware implementations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at guessing what might happen next in uncertain situations. It’s like when doctors try to diagnose a patient based on some symptoms they know about. Or when self-driving cars need to predict what will happen if they keep going straight or turn left. Right now, special computer networks called Bayesian neural networks (BayesNNs) are really good at doing this, but they use too much computer power and memory. This paper shows how to make a special kind of BayesNN that uses less energy and is faster on special computers called FPGAs. The results show that this new approach works better than other ways people have tried to do the same thing. |
Keywords
» Artificial intelligence » Dropout » Optimization
