Tech Xplore on MSN

Tiny silicon structures compute with heat, achieving 99% accurate matrix multiplication

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

Linear Regression with Pseudo-Inverse Training Using JavaScript

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 ...
Morning Overview on MSN

MIT’s heat-powered silicon chips hit 99% accuracy in math tests

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 ...
Knowridge Science Report

MIT scientists turn waste heat into computing power with 99% accuracy

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 ...
IEEE

DenSparSA: A Balanced Systolic Array Approach for Dense and Sparse Matrix Multiplication

Abstract: Numerous studies have proposed hardware architectures to accelerate sparse matrix multiplication, but these approaches often incur substantial area and power overhead, significantly ...
IEEE

Advancing Frequency Line Extraction Algorithm Through Low-Rank and Sparse Matrix Decomposition and Sliding Windowed Fusion Frame

Abstract: The frequency line is an important basis for the correlation and identification of underwater targets. However, how to accurately extract frequency line information from the interference of ...