Energy-Efficient Speaker Identification with Low-Precision Networks

Abstract

Power-consumption in small devices is dominated by off-chip memory accesses, necessitating small models that can fit in on-chip memory. In the task of text-dependent speaker identification, we demonstrate a 16x byte-size reduction for state-of-art small-footprint LCN/CNN/DNN speaker identification models. We achieve this by using ternary quantization that constrains the weights to {-1, 0, 1}. Our model comfortably fits in the 1 MB on-chip BRAM of most off-the-shelf FPGAs, allowing for a power-efficient speaker ID implementation with 100x fewer floating point multiplications, and a 1000x decrease in estimated energy cost. Additionally, we explore the use of depth-wise separable convolutions for speaker identification, and show while significantly reducing multiplications in full-precision networks, they perform poorly when ternarized. We simulate hardware designs for inference on our model, the first hardware design targeted for efficient evaluation of ternary networks and end-to-end neural network-based speaker identification.