ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA

TL;DR

This paper introduces ESE, a FPGA-based speech recognition engine that compresses LSTM models via pruning and quantization, enabling faster, energy-efficient speech recognition with high performance on hardware.

Contribution

It proposes a load-balance-aware pruning method, a scheduler for parallel processing, and a specialized FPGA architecture for efficient speech recognition on compressed LSTM models.

Findings

Achieves 20x model compression with negligible accuracy loss.

ESE processes speech recognition tasks at 2.52 TOPS on FPGA.

Outperforms CPU and GPU in speed and energy efficiency by large margins.

Abstract

Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve higher prediction accuracy, machine learning scientists have built larger and larger models. Such large model is both computation intensive and memory intensive. Deploying such bulky model results in high power consumption and leads to high total cost of ownership (TCO) of a data center. In order to speedup the prediction and make it energy efficient, we first propose a load-balance-aware pruning method that can compress the LSTM model size by 20x (10x from pruning and 2x from quantization) with negligible loss of the prediction accuracy. The pruned model is friendly for parallel processing. Next, we propose scheduler that encodes and partitions the compressed model to each PE for parallelism, and schedule the complicated LSTM data flow. Finally, we design the hardware architecture, named Efficient Speech Recognition Engine (ESE) that works directly on the compressed model. Implemented on Xilinx XCKU060 FPGA running at 200MHz, ESE has a performance of 282 GOPS working directly on the compressed LSTM network, corresponding to 2.52 TOPS on the uncompressed one, and processes a full LSTM for speech recognition with a power dissipation of 41 Watts. Evaluated on the LSTM for speech recognition benchmark, ESE is 43x and 3x faster than Core i7 5930k CPU and Pascal Titan X GPU implementations. It achieves 40x and 11.5x higher energy efficiency compared with the CPU and GPU respectively.