A paper describing the superior performance of an FPGA-based, speech-recognition implementation over similar implementations on CPUs and GPUs won a Best Paper Award at FPGA 2017 held in Monterey, CA last month. The paper—titled “ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA” and written by authors from Stanford U, DeePhi Tech, Tsinghua U, and Nvidia—describes a speech-recognition algorithm using LSTM (Long Short-Term Memory) models with load-balance-aware pruning implemented on a Xilinx Kintex UltraScale+ KU060 FPGA. The implementations runs at 200MHz and draws 41W (for the FPGA board) slotted into a PCIe chassis. Compared to Core i7 CPU/Pascal Titan X GPU implementations of the same algorithm, the FPGA-based implementation delivers 43x/3x more raw performance and 40x/11.5x better energy efficiency, according to the FPGA 2017 paper. So the FPGA implementation is both faster and more energy-efficient. Pick any two.
Here’s a block diagram of the resulting LSTM speech-recognition design:
The paper describes the algorithm and implementation in detail, which probably contributed to this paper winning the conference’s Best Paper Award. This work was supported by the National Natural Science Foundation of China.
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March 6, 2017 at 11:55PM