Summary of Energy-efficiency Limits on Training Ai Systems Using Learning-in-memory, by Zihao Chen and Johannes Leugering and Gert Cauwenberghs and Shantanu Chakrabartty
Energy-efficiency Limits on Training AI Systems using Learning-in-Memory
by Zihao Chen, Johannes Leugering, Gert Cauwenberghs, Shantanu Chakrabartty
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Learning-in-memory (LIM) is a new approach to overcome memory bottlenecks in training machine learning systems. While compute-in-memory (CIM) methods can address energy dissipation from repeated memory read access, they don’t account for the energy dissipated during memory writes and information transfer between short-term and long-term memories. The LIM paradigm proposes adaptive modulation of physical memory to match gradient-descent training dynamics. This paper derives theoretical lower bounds on energy dissipation using different LIM approaches, highlighting trade-offs between energy efficiency and training speed. The results have a similar flavor to Landauer’s energy-dissipation bounds and account for floating-point operations, model size, and precision. Projections suggest that training a brain-scale AI system using LIM requires approximately 108-109 Joules, comparable to Landauer’s adiabatic lower-bound and six to seven orders of magnitude lower than state-of-the-art hardware lower-bounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to train super smart machines that can do things like recognize pictures or understand speech. Right now, it takes a lot of energy for these machines to learn new things because they have to constantly access and update their memories. This paper proposes a way to make this process more efficient by changing how the machine accesses its memories. The authors show that this approach could be very effective, using much less energy than current methods. They even project that with this method, we could train super smart machines that can do things like recognize pictures or understand speech, but use only a tiny fraction of the energy it takes today. |
Keywords
* Artificial intelligence * Gradient descent * Machine learning * Precision
