Papers › HPTQ: Hardware-Friendly Post Training Quantization
HPTQ: Hardware-Friendly Post Training Quantization
Hai Victor Habi, Reuven Peretz, Elad Cohen, Lior Dikstein, Oranit Dror, Idit Diamant, Roy H. Jennings, Arnon Netzer
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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Quantization | COCO (Common Objects in Context) | SSD ResNet50 V1 FPN 640x640 | MAP | 34.3 | #1 of 1 | Archive leaderboard | report |
| Quantization | ImageNet | Xception W8A8 | Activation bits | 8 | #8 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | Xception W8A8 | Top-1 Accuracy (%) | 78.972 | #8 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | Xception W8A8 | Weight bits | 8 | #8 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | EfficientNet-B0 ReLU W8A8 | Activation bits | 8 | #11 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | EfficientNet-B0 ReLU W8A8 | Top-1 Accuracy (%) | 77.092 | #11 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | EfficientNet-B0 ReLU W8A8 | Weight bits | 8 | #11 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | EfficientNet-B0 W8A8 | Activation bits | 8 | #16 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | EfficientNet-B0 W8A8 | Top-1 Accuracy (%) | 74.216 | #16 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | EfficientNet-B0 W8A8 | Weight bits | 8 | #16 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | DenseNet-121 W8A8 | Activation bits | 8 | #19 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | DenseNet-121 W8A8 | Top-1 Accuracy (%) | 73.356 | #19 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | DenseNet-121 W8A8 | Weight bits | 8 | #19 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | MobileNetV2 W8A8 | Activation bits | 8 | #23 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | MobileNetV2 W8A8 | Top-1 Accuracy (%) | 71.46 | #23 of 27 | Archive leaderboard | report |
| Quantization | ImageNet | MobileNetV2 W8A8 | Weight bits | 8 | #23 of 27 | Archive leaderboard | report |
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