In the rapidly evolving Internet of Things (IoT) domain, we concentrate on enhancing energy efficiency in Deep Learning accelerators on FPGA-based heterogeneous platforms, aligning with the principles of sustainable computing. Instead of focusing on the inference phase, we introduce innovative optimizations to minimize the overhead of the FPGA configuration phase. By fine-tuning configuration parameters correctly, we achieved a 40.13-fold reduction in configuration energy. Moreover, augmented with power-saving methods, our Idle-Waiting strategy outperformed the traditional On-Off strategy in duty-cycle mode for request periods up to 499.06 ms. Specifically, at a 40 ms request period within a 4147 J energy budget, this strategy extends the system lifetime to approximately 12.39x that of the On-Off strategy. Empirically validated through hardware measurements and simulations, these optimizations provide valuable insights and practical methods for achieving energy-efficient and sustainable deployments in IoT.

翻译：在快速发展的物联网领域，我们聚焦于提升基于FPGA异构平台上深度学习加速器的能效，这与可持续计算的原则高度契合。我们不专注于推理阶段，而是引入创新性优化方法以最小化FPGA配置阶段的开销。通过正确调整配置参数，我们实现了配置能耗降低40.13倍。此外，结合节能方法，我们提出的"空闲等待"策略在占空比模式下，针对周期不超过499.06毫秒的请求场景，性能优于传统的"开关"策略。具体而言，在4147焦耳能量预算下，当请求周期为40毫秒时，该策略可将系统寿命延长至"开关"策略的约12.39倍。通过硬件测量与仿真验证，这些优化为物联网中实现高能效且可持续的部署提供了宝贵的见解与实践方法。