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 ...

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 ...

Distributed computing has markedly advanced the efficiency and reliability of complex numerical tasks, particularly matrix multiplication, which is central to numerous computational applications from ...

There has been an ever-growing demand for artificial intelligence and fifth-generation communications globally, resulting in very large computing power and memory requirements. The slowing down or ...

Nearly all big science, machine learning, neural network, and machine vision applications employ algorithms that involve large matrix-matrix multiplication. But multiplying large matrices pushes the ...

The most widely used matrix-matrix multiplication routine is GEMM (GEneral Matrix Multiplication) from the BLAS (Basic Linear Algebra Subroutines) library. And these days it can be found being used in ...

What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...