SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems

• URL: http://arxiv.org/abs/2405.03967v1
• Date: Tue, 7 May 2024 02:54:31 GMT
• Title: SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems
• Authors: Kailash Gogineni, Sai Santosh Dayapule, Juan Gómez-Luna, Karthikeya Gogineni, Peng Wei, Tian Lan, Mohammad Sadrosadati, Onur Mutlu, Guru Venkataramani,
• Abstract summary: Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward signals from experience datasets. 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.
• Score: 21.133750045141802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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