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 ...

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

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 ...

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 ...

A novel stacked memristor architecture performs Euclidean distance calculations directly within memory, enabling energy-efficient self-organizing maps without external arithmetic circuits.

Researchers at Massachusetts Institute of Technology have demonstrated a surprising new way to compute—by using heat instead of electricity. In a proof-of-concept study published in Physical Review ...