Abstract: Exploiting the numeric symmetry in sparse matrices to reduce their memory footprint is very tempting for optimizing the memory-bound Sparse Matrix-Vector Multiplication (SpMV) kernel.

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 ...

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 project is intended for research purposes only. Use it at your own risk and discretion. Triton is a language and compiler for writing highly efficient ML primitives, one of the most common ...

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 ...

New silicon designs apply AI to processing and enhancing digital audio. Cadence has new IP to simplify the work.

This implementation creates a sophisticated knowledge retrieval system by integrating KAG methodologies with traditional RAG approaches. It seamlessly combines Graphiti's graph intelligence, Qdrant's ...

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 ...