MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more ...

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, ...

Systolic arrays are fundamental building blocks for efficient matrix multiplication and ML accelerators (e.g., Google TPU, tensor cores). A reusable, well-tested systolic array library would benefit ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

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.

Abstract: Sparse Matrix-Matrix Multiplication (SpMM) and Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) are essential computational kernels in domains such as graph analytics and scientific ...

A new technical paper titled “Scalable MatMul-free Language Modeling” was published by UC Santa Cruz, Soochow University, UC Davis, and LuxiTech. “Matrix multiplication (MatMul) typically dominates ...

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, ...

School of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia Sydney Nano Institute, The University of Sydney, Sydney, NSW 2006, Australia ...