Knowledge Distillation for In-Memory Keyword Spotting Model

We study a light-weight implementation of keyword spotting (KWS) for voice command and control, that can be implemented on an in-memory computing (IMC) unit with same accuracy at a lower computational cost than the state-of-the-art methods. KWS is expected to be always-on for mobile devices with limited resources. IMC represents one of the solutions. However, it only supports multiplication-accumulation and Boolean operations. We note that common feature extraction methods, such as MFCC and SincConv, are not supported by IMC as they depend on expensive logarithm computing. On the other hand, some neural network solutions to KWS involve a large number of parameters that are not feasible for mobile devices. In this work, we propose a knowledge distillation technique to replace the complex speech frontend like MFCC or SincConv with a light-weight encoder without performance loss. Experiments show that the proposed model outperforms the KWS model with MFCC and SincConv front-end in terms of accuracy and computational cost.