Abstract
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 12.39\(\times \) 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.
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The authors acknowledge the financial support provided by the Federal Ministry of Economic Affairs and Climate Protection of Germany in the RIWWER project (01MD22007C).
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Qian, C., Cichiwskyj, C., Ling, T., Schiele, G. (2024). Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity. In: Fey, D., Stabernack, B., Lankes, S., Pacher, M., Pionteck, T. (eds) Architecture of Computing Systems. ARCS 2024. Lecture Notes in Computer Science, vol 14842. Springer, Cham. https://doi.org/10.1007/978-3-031-66146-4_11
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