Photonics is promising to handle extensive vector multiplications in AI applications. Scientists in China have promoted a programmable and reconfigurable photonic linear vector machine named SUANPAN, ...
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 ...
A team of researchers developed “parallel optical matrix-matrix multiplication” (POMMM), which could revolutionize tensor ...
Abstract: With increasing complex workflow application and computational resources requirement, distributed computing has attracted growing attention. Meanwhile, cloud computing has emerged as a ...
Abstract: The Multiply and Accumulator (MAC) in Convolution Neural Network (CNN) for image applications demands an efficient matrix multiplier. This study presents an area- and power-efficient ...
The goal of this assignment is to implement high-performance CUDA kernels for tensor operations and integrate them with the MiniTorch framework. You will implement low-level operators in CUDA C++ and ...
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