Invention Grant
- Patent Title: Error allocation format selection for hardware implementation of deep neural network
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Application No.: US17846803Application Date: 2022-06-22
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Publication No.: US11734553B2Publication Date: 2023-08-22
- Inventor: James Imber
- Applicant: Imagination Technologies Limited
- Applicant Address: GB Kings Langley
- Assignee: Imagination Technologies Limited
- Current Assignee: Imagination Technologies Limited
- Current Assignee Address: GB Kings Langley
- Agency: Potomac Law Group, PLLC
- Agent Vincent M DeLuca
- Priority: GB 18295 2017.11.03
- Main IPC: G06F11/00
- IPC: G06F11/00 ; G06N3/063 ; G06N3/084 ; G06F7/483 ; G06F17/11 ; G06N3/047 ; G06N3/048

Abstract:
Methods for determining a fixed point format for one or more layers of a DNN based on the portion of the output error of the DNN attributed to the fixed point formats of the different layers. Specifically, in the methods described herein the output error of a DNN attributable to the quantisation of the weights or input data values of each layer is determined using a Taylor approximation and the fixed point number format of one or more layers is adjusted based on the attribution. For example, where the fixed point number formats used by a DNN comprises an exponent and a mantissa bit length, the mantissa bit length of the layer allocated the lowest portion of the output error may be reduced, or the mantissa bit length of the layer allocated the highest portion of the output error may be increased. Such a method may be iteratively repeated to determine an optimum set of fixed point number formats for the layers of a DNN.
Public/Granted literature
- US20220327366A1 Error Allocation Format Selection for Hardware Implementation of Deep Neural Network Public/Granted day:2022-10-13
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