公开(公告)号：US20240176981A1

公开(公告)日：2024-05-30

申请号：US18072012

申请日：2022-11-30

Applicant: Xilinx, Inc.

Inventor： Alireza Khodamoradi ,  Kristof Denolf

IPC: G06N3/04

CPC classification number: G06N3/04

Abstract: In pruning weights from a neural network (NN), a design tool selects a dt-ds pair from a plurality of dt-ds pairs supported by a target device. Each dt-ds pair specifies a data type, dt, and an associated circuit structure, ds, that is configurable to compute d×s operations in parallel on a set of input activations and a matrix of weights of the data type, d is a number of rows in a sub-matrix of the matrix of weights, s is a number of columns in the sub-matrix, and d×s≥1. The design tool selects as pruned weights, one or more subsets of the weights, based at least on each subset of the one or more subsets including d×s weights in the matrix of weights of the layer. If performance of the pruned NN model is satisfactory, the NN is compiled into an execution graph and configuration data.

公开(公告)号：US12271818B1

公开(公告)日：2025-04-08

申请号：US17330048

申请日：2021-05-25

Applicant: XILINX, INC.

Inventor： Kristof Denolf ,  Alireza Khodamoradi ,  Kornelis A. Vissers

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/082

Abstract: Embodiments herein describe a learnable transform block disposed before, or in between, the neural network layers to transform received data into a more computational-friendly domain while preserving discriminative features required for the neural network to generate accurate results. In one embodiment, during a training phase, an AI system learns parameters for the transform block that are then used during the inference phase to transform received data into the computational-friendly domain that has a reduced size input. The transformed data may require less compute resources or less memory usage to process by the underlying hardware device that hosts the neural network.