Run-time Non-uniform Quantization for Dynamic Neural Networks in Wireless Communication
Priscilla Sharon Allwin, Manil Dev Gomony, Marc C. W. Geilen · 2024
Dynamic Neural Networks (DyNN) offer the ability to adapt their structure, parameters, or precision dynamically, making them suitable for systems with rapidly changing environmental conditions, such as wireless communication. Traditional uniform quantization, if applied in DyNNs, will result in unnecessary switching power as the precision requirements are different at different environment conditions. To address this issue, we present two main contributions. 1) An offline non-uniform quantization algorithm enabling run-time quantization adaptation while preserving system performance. 2) A low-overhead dynamic data-gating architecture facilitating run-time non-uniform quantization. The proposed algorithm facilitates dynamic data-gating of up to 8-bits for QPSK demodulation parameters with no performance loss in a Digital Video Broadcast (DVB-S.2) receiver simulation. The DyNN architecture with data-gating, synthesized using GF 22-nm FDSOI CMOS technology achieves a 43% total power reduction with a minimal 3% area overhead compared to the architecture without data-gating.