UpdatedAugust 20, 2025

MatMulInteger

Description

Matrix product that behaves like numpy.matmul. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.

Input parameters

specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.

Graphs in : cluster, ONNX model architecture.

A (heterogeneous) – T1 : object, N-dimensional matrix A.
B (heterogeneous) – T2 : object, N-dimensional matrix B.
a_zero_point (optional, heterogeneous) – T1 : object, zero point tensor for input ‘A’. It’s optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, …, zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1].
b_zero_point (optional, heterogeneous) – T2 : object, zero point tensor for input ‘B’. It’s optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, …, zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N].

Parameters : cluster,

training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.

name (optional) : string, name of the node.

Output parameters

Y (heterogeneous) – T3 : object, matrix multiply results from A * B.

Type Constraints

T1 in (tensor(int8), tensor(uint8)) : Constrain input A data type to 8-bit integer tensor.

T2 in (tensor(int8), tensor(uint8)) : Constrain input B data type to 8-bit integer tensor.

T3 in (tensor(int32)) : Constrain output Y data type as 32-bit integer tensor.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).

Quick start

Installation guide

General

Iconography

API

Architecture

Layers

Nodes

Nodes

Graph Function

Graph

File

Get & Set

Runtime

Create

Inference

Training

Academic Training

Exec

Inference

Input

Reinforcement Learning

Advanced

Add Weight

Index

Name

Format Weight

Get Weight

Index

Name

Set Weight

More

Layers parameters

Nodes Parameters