量化加速的FPGA强化学习引擎，提升性能并降低能耗。
QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine
- 结合量化与轻量架构，实现高效硬件部署。
- 相比顶尖方法，吞吐提升2.3倍，帧率提高2.6倍。
- 适合资源受限设备，支持灵活配置延迟与能效。
强化学习在序列决策和动态环境控制中表现优异，但其在FPGA上的部署面临资源开销大、计算量高的挑战。本文提出QForce-RL，利用量化技术提升吞吐量并降低能耗，同时保持近似性能。该设计融合E2HRL以减少需学习的策略动作，采用QuaRL实现基于SIMD的量化硬件加速。我们对多种强化学习环境进行了详细分析，重点考察模型规模、参数量及加速计算操作。该架构可扩展至资源受限设备，支持在延迟、吞吐、功耗与能效间灵活权衡。实验表明，QForce-RL相较当前最优方案性能提升最高达2.3倍，帧率提升2.6倍。
原文摘要 · Abstract (English)
Reinforcement Learning (RL) has outperformed other counterparts in sequential decision-making and dynamic environment control. However, FPGA deployment is significantly resource-expensive, as associated with large number of computations in training agents with high-quality images and possess new challenges. In this work, we propose QForce-RL takes benefits of quantization to enhance throughput and reduce energy footprint with light-weight RL architecture, without significant performance degradation. QForce-RL takes advantages from E2HRL to reduce overall RL actions to learn desired policy and QuaRL for quantization based SIMD for hardware acceleration. We have also provided detailed analysis for different RL environments, with emphasis on model size, parameters, and accelerated compute ops. The architecture is scalable for resource-constrained devices and provide parametrized efficient deployment with flexibility in latency, throughput, power, and energy efficiency. The proposed QForce-RL provides performance enhancement up to 2.3x and better FPS - 2.6x compared to SoTA works.
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