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 ...
A novel stacked memristor architecture performs Euclidean distance calculations directly within memory, enabling ...
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 ...
The Register on MSN
Nvidia leans on emulation to squeeze more HPC oomph from AI chips in race against AMD
AMD researchers argue that, while algorithms like the Ozaki scheme merit investigation, they're still not ready for prime ...
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 ...
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.
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.
Given the rapidly evolving landscape of Artificial Intelligence, one of the biggest hurdles tech leaders often come across is transitioning from being “experimental” to being “enterprise-ready”. While ...
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 ...
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