Hardware accelerators for deep reinforcement learning

Vinod K. Mishra; Kanad Basu; Ayush Arunachalam

doi:10.1117/12.2663175

12 June 2023 Hardware accelerators for deep reinforcement learning

Vinod K. Mishra, Kanad Basu, Ayush Arunachalam

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125381C (2023) https://doi.org/10.1117/12.2663175
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Recently, deep reinforcement learning (DRL) algorithms have been adapted for real-time control and policy-based decision for robots, drones, and autonomous vehicles. Traditional implementations of DRL use general purpose computing (CPU) and graphical processing units (GPU). In current work, we present HardCompress as an optimized hardware configuration for neural network (DNN) accelerators using High Level Synthesis (HLS) techniques.

Conference Presentation

Citation Download Citation

Vinod K. Mishra, Kanad Basu, and Ayush Arunachalam "Hardware accelerators for deep reinforcement learning", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125381C (12 June 2023); https://doi.org/10.1117/12.2663175

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