  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.
