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

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: On multicore architectures, the ratio of peak memory bandwidth to peak floating-point performance (byte:flop ratio) is decreasing as core counts increase, further limiting the performance of ...

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

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 ...

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 ...

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 ...

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

A novel stacked memristor architecture performs Euclidean distance calculations directly within memory, enabling ...

Numerical computation and mathematical software form the backbone of modern scientific inquiry, facilitating the approximation of real numbers, the solution of complex mathematical models, and the ...