Accelerating PoT Quantization on Edge Devices

• URL: http://arxiv.org/abs/2409.20403v2
• Date: Tue, 22 Oct 2024 00:12:40 GMT
• Title: Accelerating PoT Quantization on Edge Devices
• Authors: Rappy Saha, Jude Haris, José Cano,
• Abstract summary: Non-uniform quantization, such as power-of-two (PoT) quantization, matches data distributions better than uniform quantization. Existing pipelines for accelerating PoT-quantized Deep Neural Networks on edge devices are not open-source. We propose PoTAcc, an open-source pipeline for end-to-end acceleration of PoT-quantized DNNs on resource-constrained edge devices.
• Score: 0.9558392439655012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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