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HPTQ: Hardware-Friendly Post Training Quantization

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arxiv 2109.09113 v3 pith:5MC6ZZDR submitted 2021-09-19 cs.CV cs.AI

Hai Victor Habi , Reuven Peretz , Elad Cohen , Lior Dikstein , Oranit Dror , Idit Diamant , Roy H. Jennings , Arnon Netzer This is my paper
classification cs.CVcs.AI
keywords quantizationhardware-friendlyconstraintshptqmethodsnetworkposttraining
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Neural network quantization enables the deployment of models on edge devices. An essential requirement for their hardware efficiency is that the quantizers are hardware-friendly: uniform, symmetric, and with power-of-two thresholds. To the best of our knowledge, current post-training quantization methods do not support all of these constraints simultaneously. In this work, we introduce a hardware-friendly post training quantization (HPTQ) framework, which addresses this problem by synergistically combining several known quantization methods. We perform a large-scale study on four tasks: classification, object detection, semantic segmentation and pose estimation over a wide variety of network architectures. Our extensive experiments show that competitive results can be obtained under hardware-friendly constraints.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation

    cs.CV 2026-03 conditional novelty 5.0 of 10

    A 1.3M-parameter CNN with ROI-implicit prompting and SAM3 distillation reaches ~65% mIoU on COCO/LVIS and 11.82 ms INT8 inference fully in-sensor on the Sony IMX500.

  2. EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices

    cs.CV 2025-06 reject novelty 4.0 of 10

    EfficientQuant applies uniform weight quantization to CNN blocks and logarithmic activation quantization to transformer blocks in hybrid models, reporting latency reductions of 2.5x to 8.7x with modest accuracy loss.

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