Deep Reinforcement Learning Processor Design for Mobile Applications
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Beschreibung
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-limited mobile devices. Methods are described that enable DRL optimization at the algorithm-, architecture-, and circuit-levels of abstraction.
- Enables deep reinforcement learning (DRL) optimization at algorithm-, architecture-, and circuit-levels of abstraction;
- Includes methodologies that can reduce the high cost of DRL;
- Uses analysis of computational workload characteristics of DRL in the context of acceleration.
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-limited mobile devices. Methods are described that enable DRL optimization at the algorithm-, architecture-, and circuit-levels of abstraction.
Enables deep reinforcement learning (DRL) optimization at algorithm-, architecture-, Includes methodologies that can reduce the high cost of DRL Uses analysis of computational workload characteristics of DRL in the context of acceleration
Autor*in
Juhyoung Lee
Themen in »Deep Reinforcement Learning Processor Design for Mobile Applications«
Artificial intelligence Deep neural network Deep Reinforcement Learning AI Semiconductor AI ASIC