A novel stacked memristor architecture performs Euclidean distance calculations directly within memory, enabling ...
AMD researchers argue that, while algorithms like the Ozaki scheme merit investigation, they're still not ready for prime ...
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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 ...
New silicon designs apply AI to processing and enhancing digital audio. Cadence has new IP to simplify the work.
Understanding the benefits of matrix converters for EV chargers and a comparison of different matrix converter topologies.
Given the rapidly evolving landscape of Artificial Intelligence, one of the biggest hurdles tech leaders often come across is ...
Abstract: Sparse matrix-vector multiplication (SpMV) is a fundamental operation in machine learning, scientific computing, and graph algorithms. In this paper, we investigate the space, time, and ...
Silicon photonics is the study of the optical properties of the group-IV semiconductor and the design and fabrication of devices for generating, manipulating and detecting light. Silicon is prevalent ...
Abstract: Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse operation in scientific computing and artificial intelligence. Efficiently adapting SpMV algorithms to diverse matrices and ...
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 ...
Quantum-inspired adaptive tiling for high-performance matrix multiplication. Uses WKB tunneling physics with the golden ratio to derive optimal tile sizes from real-time CPU state. 15%+ gains on ...
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