Summary of Explainable Fuzzy Neural Network with Multi-fidelity Reinforcement Learning For Micro-architecture Design Space Exploration, by Hanwei Fan et al.
Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration
by Hanwei Fan, Ya Wang, Sicheng Li, Tingyuan Liang, Wei Zhang
First submitted to arxiv on: 14 Dec 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 The proposed framework combines Fuzzy Neural Networks with multi-fidelity reinforcement learning to improve design space exploration (DSE) in micro-architecture design. The existing DSE algorithms, such as Bayesian Optimization and ensemble learning, lack interpretability, making it challenging for designers to understand the decision-making process. By inducing and summarizing knowledge from the DSE process, Fuzzy Neural Networks enhance interpretability and controllability. Additionally, the multi-fidelity reinforcement learning approach efficiently explores the design space using cheap but less precise data, reducing reliance on costly data. Experimental results demonstrate excellent performance with a limited sample budget and surpass state-of-the-art benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to help computer chip designers by using special kinds of neural networks (Fuzzy Neural Networks) to understand what’s happening during the design process. Right now, existing methods don’t provide clear explanations for why certain designs are chosen. By making this process more transparent, designers can make better decisions and improve their creations. The paper also introduces a new way to explore the vast space of possible chip designs using less precise data first, which saves time and resources. This approach achieves impressive results with minimal effort. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
