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Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey
Author1, Author2, Author3, Author4, Author5
FPGAreinforcement learninghardware optimizationenergy efficiency
2610.28699
Builder Relevance
2h ago80%
Abstract
This survey reviews over 100 studies on reinforcement learning implementations across FPGA and IC platforms, focusing on architectures, strategies, and performance evaluations.
Reality Card
Core Claim
The survey provides a unified analysis of RL implementations on FPGA and IC platforms, highlighting architectural patterns and performance metrics.
Method / Result
The review covers more than 100 studies, offering comparative assessments of resource utilization, latency, throughput, and power consumption.
Limitations
Challenges in resource constraints and implementation complexity may hinder reproducibility.
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