Summary of Fames: Fast Approximate Multiplier Substitution For Mixed-precision Quantized Dnns–down to 2 Bits!, by Yi Ren et al.
FAMES: Fast Approximate Multiplier Substitution for Mixed-Precision Quantized DNNs–Down to 2 Bits!
by Yi Ren, Ruge Xu, Xinfei Guo, Weikang Qian
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET)
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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 explores the application of approximate multipliers (AppMuls) in designing energy-efficient deep neural network (DNN) accelerators with very low bitwidths. The authors target the intersection of quantization and AppMuls, where prior works have assumed larger bitwidths. They present FAMES, a fast approximate multiplier substitution method for mixed-precision DNNs, achieving an average 28.67% energy reduction on state-of-the-art models with bitwidths as low as 2 bits while maintaining accuracy losses under 1%. This breakthrough is up to 300x faster than previous genetic algorithm-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in making computers use less energy when doing important tasks like recognizing pictures or understanding speech. Right now, people are trying to make these computers work with tiny amounts of energy, but it’s hard because they need to do lots of calculations. The authors found a way to make those calculations faster and more efficient by using special tools called approximate multipliers. This helps reduce the energy used by 28.67% while still keeping the results very accurate. |
Keywords
» Artificial intelligence » Neural network » Precision » Quantization
