Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs. (arXiv:2310.16842v1 [cs.AR])

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 textit{XC7S15} from textit{Spartan-7} family. It
achieves at least 5.4$times$ faster throughput and 1.37$times$ more energy
efficient than existing approaches.