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 ...
A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...
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 ...
Interesting Engineering on MSN
MIT’s new heat-powered silicon chips achieve 99% accuracy in math calculations
Scientists in the US have created a tiny silicon chip that can perform mathematical ...
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 ...
Abstract: Sparse General Matrix-Matrix Multiplication (SpGEMM) is a core operation in high-performance computing applications such as algebraic multigrid solvers, machine learning, and graph ...
Abstract: In the field of information security research, Arnold scrambling algorithm is an important encryption algorithm. However, due to its periodicity, when the encryption iteration reaches a ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results