Summary of Swiftrl: Towards Efficient Reinforcement Learning on Real Processing-in-memory Systems, by Kailash Gogineni et al.
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems
by Kailash Gogineni, Sai Santosh Dayapule, Juan Gómez-Luna, Karthikeya Gogineni, Peng Wei, Tian Lan, Mohammad Sadrosadati, Onur Mutlu, Guru Venkataramani
First submitted to arxiv on: 7 May 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 This paper proposes SwiftRL, a Reinforcement Learning (RL) framework that leverages Processing-In-Memory (PIM) architectures to accelerate RL workloads. The authors address memory limitations in traditional RL training by implementing Tabular Q-learning and SARSA algorithms on UPMEM PIM systems and optimizing for hardware. Experimental results on OpenAI GYM environments demonstrate superior performance compared to CPU and GPU implementations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn new things by using special memories inside the computer. The problem is that these computers can’t remember everything, so it takes a long time to learn. To fix this, the researchers created a way to make these memories work better for learning. They tested their idea on games and showed that it’s much faster than usual ways of doing things. |
Keywords
» Artificial intelligence » Reinforcement learning
