Abstract:
Activation functions (AFs) such as sigmoid and tanh play an important role in neural networks (NNs). Their efficient implementation is critical for always-on edge devices...Show MoreMetadata
Abstract:
Activation functions (AFs) such as sigmoid and tanh play an important role in neural networks (NNs). Their efficient implementation is critical for always-on edge devices. In this work, we propose a serial-arithmetic architecture for AFs in edge audio applications using the CORDIC algorithm. The design enables to dynamically trade-off throughput/latency and accuracy, and pos-sesses higher area and power efficiency compared to conventional methods such as look-up table (LUT) and piece-wise linear (PWL)-based methods. Considering the throughput difference among the designs, we evaluate average power consumption taking into account active and idle working cycles for same applications. Synthesis results in a \mathbf{22}\mathbf{nm} process show that our CORDIC-based design has an area of 545.77 \boldsymbol{\mu} \mathbf{m}^{2} and an average power of 0.69 \boldsymbol{\mu} \mathbf{W} for a keyword spotting task, achieving a reduction of 36.92% and 71.72% in average power consumption compared to LUT and PWL-based implementations, respectively.
Date of Conference: 17-19 April 2023
Date Added to IEEE Xplore: 02 June 2023
Print on Demand(PoD) ISBN:979-8-3503-9624-9