Background Noise Adaptive Energy-Efficient Keywords Recognition Processor With Reusable DNN and Reconfigurable Architecture

Abstract

This paper proposes a background noise adaptive energy-efficient keywords recognition processor with Reusable DNN (RDNN) and reconfigurable architecture. To reduce power consumption while maintaining the recognition accuracy of different background noises, the SNR prediction module determines whether the computing mode is low power consumption mode (LPM) or high performance mode (HPM). In LPM, DNN-shift (shift-based deep neural network) is used to achieve high recognition accuracy in a low background noise environment; in HPM, DNN-8bit (8bit weighted deep neural network) is used to achieve low power consumption in a high background noise environment. And the two modes share most of the hardware, and approximate computing is introduced to further reduce power consumption. Evaluated under 22nm process technology, this work can support up to 10 keywords recognition with the power consumption of $11.2~\mu \text{W}$ for high background noise and $7.3~\mu \text{W}$ for low background noise.

Keywords

• Keywords recognition
• SNR prediction module
• background noise adaptive
• approximate computing