A Mixed-Precision RNS DNN Accelerator

The Residue Number System (RNS) has been used for the design of Deep Neural Network (DNN) processing architectures due to its efficient implementation of the multiply-accumulate (MAC) operation. Prior-art RNS DNN accelerators have demonstrated notable benefits compared to conventional fixed-point (FXP) representations for arithmetic precisions of at least 8 bits. However, advanced quantization techniques have recently enabled accurate ultra-low-precision FXP DNN inference. Thus, it remains an open research question whether RNS can still outperform FXP representations for smaller precisions and especially in mixed-precision (MXP) quantization settings, where optimal bit-width configurations with respect to overall accuracy drop constraints are sought. This work addresses this gap by presenting an RNS-based MXP DNN accelerator that supports 3-8-bit quantization and consistently achieves superior model performance vs. hardware cost tradeoffs for various DNN models, resulting in up to 1.2× energy efficiency improvements compared to the FXP counterpart. Synthesized on a 22-nm technology, the RNS MXP accelerator achieves 6.93-14.58 TOPS/W, outperforming the state-of-the-art uniform-precision RNS accelerator by 1.4× while maintaining the original model accuracy, as well as mixed-precision FXP accelerators.