Accelerating PoT Quantization on Edge Devices
This work provides an open-source solution for improving efficiency in deploying quantized models on resource-constrained edge devices, though it is incremental as it builds on existing PoT quantization methods.
The paper tackles the lack of open-source pipelines for accelerating power-of-two (PoT) quantized deep neural networks on edge devices by designing shift-based processing elements and an accelerator, achieving average speedups of 1.23x and 2.46x with energy reductions of 1.24x and 1.83x compared to multiplier-based and CPU-only executions, respectively.
Non-uniform quantization, such as power-of-two (PoT) quantization, matches data distributions better than uniform quantization, which reduces the quantization error of Deep Neural Networks (DNNs). PoT quantization also allows bit-shift operations to replace multiplications, but there are limited studies on the efficiency of shift-based accelerators for PoT quantization. Furthermore, existing pipelines for accelerating PoT-quantized DNNs on edge devices are not open-source. In this paper, we first design shift-based processing elements (shift-PE) for different PoT quantization methods and evaluate their efficiency using synthetic benchmarks. Then we design a shift-based accelerator using our most efficient shift-PE and propose PoTAcc, an open-source pipeline for end-to-end acceleration of PoT-quantized DNNs on resource-constrained edge devices. Using PoTAcc, we evaluate the performance of our shift-based accelerator across three DNNs. On average, it achieves a 1.23x speedup and 1.24x energy reduction compared to a multiplier-based accelerator, and a 2.46x speedup and 1.83x energy reduction compared to CPU-only execution. Our code is available at https://github.com/gicLAB/PoTAcc