METHOD FOR AUTOMATIC HYBRID QUANTIZATION OF DEEP ARTIFICIAL NEURAL NETWORKS
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United States of America
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Issued Date -
Apr 10, 2025
app pub date -
Dec 17, 2024
filing date -
Dec 17, 2024
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Abstract
A method includes, for each floating-point layer in a set of floating-point layers: calculating a set of input activations and a set of output activations of the floating-point layer; converting the floating-point layer to a low-bit-width layer; calculating a set of low-bit-width output activations based on the set of input activations; and calculating a per-layer deviation statistic of the low-bit-width layer. The method also includes ordering the set of low-bit-width layers based on the per-layer deviation statistic of each low-bit-width layer. The method additionally includes, while a loss-of-accuracy threshold exceeds the accuracy of the quantized network: converting a floating-point layer represented by the low-bit-width layer to a high-bit-width layer; replacing the low-bit-width layer with the high-bit-width layer in the quantized network; updating the accuracy of the quantized network; and, in response to the accuracy of the quantized network exceeding the loss-of-accuracy threshold, returning the quantized network.

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Patent Owner(s)
Patent Owner | Address | |
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DEEP VISION INC | 2445 AUGUSTINE DRIVE SUITE 150 SANTA CLARA CA 95054 |
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- [Patents Count]
Inventor(s)
Inventor Name | Address | # of filed Patents | Total Citations |
---|---|---|---|
Ghanore, Abhilash Bharath | Hyderabad, IN | 9 | 8 |
Hameed, Rehan | Palo Alto, US | 18 | 130 |
Qadeer, Wajahat | Campbell, US | 11 | 78 |
Ram, Kasanagottu Sai | Hyderabad, IN | 3 | 1 |
Uppalapati, Satyanarayana Raju | Hyderabad, IN | 4 | 2 |
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