在边缘设备上加速PoT量化
Accelerating PoT Quantization on Edge Devices
AI总结：
本文提出开源流程PoTAcc，通过设计基于位移的处理单元和加速器，在边缘设备上端到端加速PoT量化DNN，实现显著加速与能耗降低。
摘要：
非均匀量化，例如2的幂（PoT）量化，比均匀量化更符合数据分布，从而降低了深度神经网络（DNN）的量化误差。PoT量化还允许用位移操作替代乘法，但关于基于位移的加速器在PoT量化中的效率研究仍然有限。此外，现有用于在边缘设备上加速PoT量化DNN的流程并非开源。在本文中，我们首先为不同的PoT量化方法设计了基于位移的处理单元（shift-PE），并使用合成基准评估其效率。然后，我们使用最高效的shift-PE设计了一个基于位移的加速器，并提出了PoTAcc，这是一个开源流程，用于在资源受限的边缘设备上对PoT量化DNN进行端到端加速。利用PoTAcc，我们在三个DNN上评估了基于位移的加速器的性能。平均而言，与基于乘法器的加速器相比，它实现了1.23倍的加速和1.24倍的能耗降低；与仅使用CPU执行相比，实现了2.46倍的加速和1.83倍的能耗降低。我们的代码可在https://github.com/gicLAB/PoTAcc获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
