METHOD FOR AUTOMATIC HYBRID QUANTIZATION OF DEEP ARTIFICIAL NEURAL NETWORKS

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United States of America

APP PUB NO 20250117638A1
SERIAL NO

18984655

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Abstract

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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)

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DEEP VISION INC2445 AUGUSTINE DRIVE SUITE 150 SANTA CLARA CA 95054

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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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