A team of researchers developed “parallel optical matrix-matrix multiplication” (POMMM), which could revolutionize tensor ...
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 ...
The key idea is that temperature differences act as inputs, and the resulting heat diffusion produces the output ...
At a recent Bengaluru mixer hosted by E2E Networks, NVIDIA, and YourStory, founders and ecosystem builders unpacked the real ...
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 ...
Sparse matrix-matrix multiplication (SpMM) is a crucial kernel in various applications, including sparse deep neural networks [1]–[6], graph analytics [7], triangle counting [8], and linear algebra ...
Abstract: Matrix multiplication is a fundamental computational operation widely used in various engineering applications. To accelerate large-scale matrix multiplication, computing tasks are commonly ...
AMD is preparing a significant redesign of its CPU architecture with the upcoming Zen 6 generation, signaling a clear break ...
Proof of concept uses passive components to redirect heat across a chip, allowing temperature patterns to be used for data processing.
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