Titlebook: Deep Reinforcement Learning Processor Design for Mobile Applications; Juhyoung Lee,Hoi-Jun Yoo Book 2023 The Editor(s) (if applicable) and

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抑制

Book 2023 (AI). The authors address acceleration systems which enable DRL on area-limited & battery-limited mobile devices.Methods are described that enable DRL optimization at the algorithm-, architecture-, and circuit-levels of abstraction..

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GONG

Deep Reinforcement Learning Processor Design for Mobile Applications,ltaneously. In this chapter, we propose a deep reinforcement learning accelerator that optimizes memory bandwidth and memory power consumption. The proposed accelerator optimizes memory bandwidth by dual-mode weight compression and optimizes memory power consumption with floating-point in-memory computing architecture.

尊嚴(yán)

DRL.Uses analysis of computational workload characteristics.This book discusses the acceleration of deep reinforcement learning (DRL), which may be the next step in the burst success of artificial intelligence (AI). The authors address acceleration systems which enable DRL on area-limited & battery

尊嚴(yán)

Deep Reinforcement Learning Processor Design for Mobile Applications

滑稽

Deep Reinforcement Learning Processor Design for Mobile Applications978-3-031-36793-9

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