Hartwig Adam, Bo Chen, Andrew Howard, Menglong Zhu, Dmitry Kalenichenko, Benoit Jacob, Skirmantas Kligys, Matthew Tang
We lifted 7 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jameszampa/ECE-570-Implementation | — | 4 of 4 |
| linyang-zhh/FQ-ViT | — | 2 of 2 |
| MaximIntegratedAI/ai8x-training | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| BaseQuantizer | Ran | linyang-zhh/FQ-ViT/models/ptq/quantizer/uniform.py code served (permissive licence) · get_code("6c8c5613a41846a2") |
| FullyConnected | Ran | jameszampa/ECE-570-Implementation/python/integer_inference.py pointer only (licence: NONE) · get_code("40771743ce834305") |
| MultiplyByQuantizedMultiplierSmallerThanOne | Ran | jameszampa/ECE-570-Implementation/python/integer_inference.py pointer only (licence: NONE) · get_code("9c24f357578474e5") |
| QuantizationFunction | Ran | MaximIntegratedAI/ai8x-training/ai8x.py code served (permissive licence) · get_code("fbe27ee7dc10aa67") |
| RoundingDivideByPOT | Ran | jameszampa/ECE-570-Implementation/python/integer_inference.py pointer only (licence: NONE) · get_code("f80d39ab0932d302") |
| SaturatingRoundingDoublingHighMul | Ran | jameszampa/ECE-570-Implementation/python/integer_inference.py pointer only (licence: NONE) · get_code("98ec162d7259fc3b") |
| UniformQuantizer | Ran | linyang-zhh/FQ-ViT/models/ptq/quantizer/uniform.py code served (permissive licence) · get_code("476098355ebe4558") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1712.05877")
get_code_for_paper("1712.05877")
have("1712.05877")
Connect an agent — have() is free.