Low-bit Shift Network for End-to-End Spoken Language Understanding. (arXiv:2207.07497v1 [cs.SD])

Deep neural networks (DNN) have achieved impressive success in multiple
domains. Over the years, the accuracy of these models has increased with the
proliferation of deeper and more complex architectures. Thus, state-of-the-art
solutions are often computationally expensive, which makes them unfit to be
deployed on edge computing platforms. In order to mitigate the high
computation, memory, and power requirements of inferring convolutional neural
networks (CNNs), we propose the use of power-of-two quantization, which
quantizes continuous parameters into low-bit power-of-two values. …

arxiv language network shift spoken language understanding understanding