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PoTAcc pipeline accelerates power-of-two quantized DNNs on edge devices

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Researchers have developed PoTAcc, an open-source pipeline designed to accelerate the deployment of power-of-two (PoT) quantized deep neural networks (DNNs) on resource-constrained edge devices. This system facilitates the preparation and deployment of these models through TensorFlow Lite, supporting both CPU-only configurations and hybrid CPU-FPGA systems with custom accelerators. Evaluations demonstrated that a CPU-accelerator design using PoTAcc achieved up to a 3.6x speedup and a 78% reduction in energy consumption compared to CPU-only execution on specific FPGA boards. AI

IMPACT Accelerates the deployment of quantized DNNs on edge devices, potentially improving performance and energy efficiency for AI applications in resource-constrained environments.

RANK_REASON This is a research paper detailing a new pipeline for accelerating quantized deep neural networks on edge devices. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PoTAcc pipeline accelerates power-of-two quantized DNNs on edge devices

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This is a research paper detailing a new pipeline for accelerating quantized deep neural networks on edge devices. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rappy Saha, Jude Haris, Nicolas Bohm Agostini, David Kaeli, Jos\'e Cano ·

    PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs

    arXiv:2605.06082v1 Announce Type: cross Abstract: Power-of-two (PoT) quantization significantly reduces the size of deep neural networks (DNNs) and replaces multiplications with bit-shift operations for inference. Prior work has shown that PoT-quantized DNNs can preserve accuracy…

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