Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs

• URL: http://arxiv.org/abs/2310.16842v2
• Date: Sat, 25 Nov 2023 14:27:39 GMT
• Title: Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs
• Authors: Chao Qian, Tianheng Ling, Gregor Schiele
• Abstract summary: This work proposes a novel LSTM cell optimisation aimed at energy-efficient inference on end devices. It achieves at least 5.4$times$ faster throughput and 1.37$times$ more energy efficient than existing approaches.
• Score: 22.293462679874008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique. In the past, CNNs were frequently used because they are simple to optimise for special embedded hardware such as FPGAs. This work proposes a novel LSTM cell optimisation aimed at energy-efficient inference on end devices. Using the traffic speed prediction as a case study, a vanilla LSTM model with the optimised LSTM cell achieves 17534 inferences per second while consuming only 3.8 $\mu$J per inference on the FPGA XC7S15 from Spartan-7 family. It achieves at least 5.4$\times$ faster throughput and 1.37$\times$ more energy efficient than existing approaches.

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