Accelerating PoT Quantization on Edge Devices
<|reference_start|>Accelerating PoT Quantization on Edge Devices: 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<|reference_end|>
@article{saha2024accelerating,
    title={Accelerating PoT Quantization on Edge Devices},
    author={Rappy Saha, Jude Haris, Jos'e Cano},
    journal={arXiv preprint arXiv:2409.20403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20403},
    primaryClass={cs.AR cs.LG}
}