SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems
<|reference_start|>SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems: Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward signals from experience datasets. However, RL training often faces memory limitations, leading to execution latencies and prolonged training times. To overcome this, SwiftRL explores Processing-In-Memory (PIM) architectures to accelerate RL workloads. We achieve near-linear performance scaling by implementing RL algorithms like Tabular Q-learning and SARSA on UPMEM PIM systems and optimizing for hardware. Our experiments on OpenAI GYM environments using UPMEM hardware demonstrate superior performance compared to CPU and GPU implementations.<|reference_end|>
@article{gogineni2024swiftrl:,
    title={SwiftRL: Towards Efficient Reinforcement Learning on Real
  Processing-In-Memory Systems},
    author={Kailash Gogineni, Sai Santosh Dayapule, Juan G'omez-Luna, Karthikeya
  Gogineni, Peng Wei, Tian Lan, Mohammad Sadrosadati, Onur Mutlu, Guru
  Venkataramani},
    journal={arXiv preprint arXiv:2405.03967},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03967},
    primaryClass={cs.LG cs.AI cs.AR}
}