Abstract: In this work, one novel dual-gate 2T0C is proposed for multi-bit input vector-matrix multiplication (VMM) in in-memory computing (IMC). Unlike the existing pulse-width modulation (PWM) ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.

I have investigated the symptoms of this in some detail but have not tried to find the cause: In short it seems like matrix multiplications with largeish numbers fails inconsistently in windows, and ...

Matrix multiplication (MatMul) is a fundamental operation in most neural networks, primarily because GPUs are highly optimized for these computations. Despite its critical role in deep learning, ...

Abstract: This paper presents ternary systolic array archi-tecture for matrix multiplication for ternary neural networks and image processing algorithms in ternary logic. As part of the architecture, ...

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...

Computer scientists are a demanding bunch. For them, it’s not enough to get the right answer to a problem — the goal, almost always, is to get the answer as efficiently as possible. Take the act of ...