MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat ...

MIT engineers use heat-conducting silicon microstructures to perform matrix multiplication with >99% accuracy hinting at ...

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

Engineers at MIT have turned one of computing’s biggest headaches, waste heat, into the main act. By sculpting “dust-sized” silicon structures that steer heat as precisely as electrical current, they ...

Scientists in the US have created a tiny silicon chip that can perform mathematical ...

Abstract: For many scientific applications, dense matrix multiplication is one of the most important and computation intensive linear algebra operations. An efficient matrix multiplication on high ...

Abstract: We propose COSMA: a parallel matrix-matrix multiplication algorithm that is near communication-optimal for all combinations of matrix dimensions, processor counts, and memory sizes. The key ...

Researchers at Massachusetts Institute of Technology have demonstrated a surprising new way to compute—by using heat instead ...

Understanding the benefits of matrix converters for EV chargers and a comparison of different matrix converter topologies.

This repository contains the artifact for the SC '25 paper submission "KAMI: Communication-Avoiding General Matrix Multiplication within a Single GPU." The NVIDIA GH200 is installed with Ubuntu 22.04 ...