Summary of Efficient and Mathematically Robust Operations For Certified Neural Networks Inference, by Fabien Geyer et al.
Efficient and Mathematically Robust Operations for Certified Neural Networks Inference
by Fabien Geyer, Johannes Freitag, Tobias Schulz, Sascha Uhrig
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning and neural networks have revolutionized various domains, including transportation for achieving autonomy in urban air mobility (UAM). However, concerns about certification have driven the need for standardized processes throughout the ML and NN pipeline. This paper focuses on the inference stage and required hardware, highlighting challenges related to IEEE 754 floating-point arithmetic and proposing alternative number representations. By evaluating diverse summation and dot product algorithms, we aim to mitigate issues with non-associativity. Our exploration of fixed-point arithmetic reveals its advantages over floating-point methods, demonstrating significant hardware efficiencies. We employ an empirical approach to determine the optimal bit-width necessary for acceptable accuracy, considering the complexity of bit-width optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Flying taxis are becoming a reality! But before they take to the skies, we need to make sure they’re safe and reliable. One important step is to develop standardized processes for machine learning and neural networks. This paper looks at how we can improve the “inference stage” – where machines learn from data – by using better number representations and algorithms. It also explores fixed-point arithmetic as a more efficient alternative to floating-point methods. By doing so, it helps us find the right balance between accuracy and hardware efficiency. |
Keywords
* Artificial intelligence * Dot product * Inference * Machine learning * Optimization
