Summary of Hardware-aware Neural Dropout Search For Reliable Uncertainty Prediction on Fpga, by Zehuan Zhang et al.
Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA
by Zehuan Zhang, Hongxiang Fan, Hao Mark Chen, Lukasz Dudziak, Wayne Luk
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposes a novel framework for optimizing dropout-based Bayesian Neural Networks (BayesNNs) for trustworthy AI applications. The existing dropout-based BayesNNs employ uniform dropout designs across layers, which can lead to suboptimal performance. To address this challenge, the authors introduce a neural dropout search framework that automatically optimizes both the BayesNNs and their hardware implementations on Field-Programmable Gate Arrays (FPGA). The framework uses one-shot supernet training with an evolutionary algorithm for efficient dropout optimization. The paper also introduces a layer-wise dropout search space to enable automatic design of dropout-based BayesNNs with heterogeneous dropout configurations. Experimental results show that the proposed framework can effectively find design configurations on the Pareto frontier, achieving higher energy efficiency and better performance compared to state-of-the-art FPGA designs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create more trustworthy AI by making sure it’s accurate and reliable. Right now, AI systems use something called dropout-based Bayesian Neural Networks (BayesNNs) to estimate uncertainty. But there’s a problem – the way they do this isn’t very good. The authors of this paper came up with a new way to optimize these BayesNNs so that they work better and are more reliable. They used a special kind of training called one-shot supernet training, which helps them find the best combination of settings for the BayesNNs. This means we can use AI in more places, like on devices that don’t need a lot of power. |
Keywords
* Artificial intelligence * Dropout * One shot * Optimization
