The deep neural networks, such as the Deep-FSMN, have been widely studied for
keyword spotting (KWS) applications. However, computational resources for these
networks are significantly constrained since they usually run on-call on edge
devices. In this paper, we present BiFSMN, an accurate and extreme-efficient
binary neural network for KWS. We first construct a High-frequency Enhancement
Distillation scheme for the binarization-aware training, which emphasizes the
high-frequency information from the full-precision network's representation
that is more crucial for the optimization of the binarized network. Then, to
allow the instant and adaptive accuracy-efficiency trade-offs at runtime, we
also propose a Thinnable Binarization Architecture to further liberate the
acceleration potential of the binarized network from the topology perspective.
Moreover, we implement a Fast Bitwise Computation Kernel for BiFSMN on ARMv8
devices which fully utilizes registers and increases instruction throughput to
push the limit of deployment efficiency. Extensive experiments show that BiFSMN
outperforms existing binarization methods by convincing margins on various
datasets and is even comparable with the full-precision counterpart (e.g., less
than 3% drop on Speech Commands V1-12). We highlight that benefiting from the
thinnable architecture and the optimized 1-bit implementation, BiFSMN can
achieve an impressive 22.3x speedup and 15.5x storage-saving on real-world edge
hardware. Our code is released at this https URL.

本研究提出了 BiFSMN，一种精度高、效率极高的二进制神经网络，它包括了针对一些关键信息的深度蒸馏机制、一种优化的二进制化训练方法、可以自适应提高通用性和效率的纤薄化结构和一个在 ARMv8 设备上的快速位运算核心，实现了在各种数据集上完备性和效率上的优化。 BiFSMN 可以在实际硬件中获得惊人的 22.3 倍加速和 15.5 倍存储空间节省。