TTT-Discover optimizes GPU kernels 2x faster than human experts — by training during inference

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

Efficient Characteristic Mode Analysis Based on Data-Sparse Matrix Algorithm

Abstract: The implicitly restarted Arnoldi method (IRAM), which relies on Krylov subspace iteration, is an effective approach for extracting desired partial eigenpairs in characteristic mode analysis ...
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 ...
Tech Xplore

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 instead of electricity. These tiny structures could someday enable more ...
IEEE

ScanNow: A Scan Window-Based Sparse Matrix Multiplication Accelerator Design

Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...