# Accelerating PoT Quantization on Edge Devices

arXiv: https://arxiv.org/abs/2409.20403 · HTML: https://mlsanity.com/paper/2409.20403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights](https://mlsanity.com/paper/1702.03044/) (2017)
- [Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks](https://mlsanity.com/paper/1909.13144/) (2019)
- [Convolutional Neural Networks using Logarithmic Data Representation](https://mlsanity.com/paper/1603.01025/) (2016)
- [DeepShift: Towards Multiplication-Less Neural Networks](https://mlsanity.com/paper/1905.13298/) (2019)
- [Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework](https://mlsanity.com/paper/2012.04240/) (2020)
- [Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks](https://mlsanity.com/paper/2203.05025/) (2022)
- [ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks](https://mlsanity.com/paper/1706.02393/) (2017)
- [SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference](https://mlsanity.com/paper/2110.00478/) (2021)
- [Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/2109.03513/) (2021)
- [Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation](https://mlsanity.com/paper/2209.15257/) (2022)

## Built on by (most cited first, 1 of 1)

- [PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs](https://mlsanity.com/paper/2605.06082/) (2026)

## Abstract (arXiv)

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