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 ...

A new publication from Opto-Electronic Technology; DOI   10.29026/oet.2025.250011, discusses integrated photonic synapses, neurons, memristors, and neural networks for photonic neuromorphic computing.

In A Nutshell Dissolving microneedle patches embedded with microscopic bubbles deliver three acne medications simultaneously, ...

Traditional technical debt metaphors suggest something that can be paid down incrementally. Over-engineering does not behave ...

Abstract: Efficiently synthesizing an entire application that consists of multiple algorithms for hardware implementation is a very difficult and unsolved problem. One of the main challenges is the ...

The authors present convincing data to validate abscisic acid-induced dimerisation to induce a synthetic spindle assembly checkpoint (SAC) arrest that will be of particular importance to analyse ...

Abstract: General matrix multiplication (GEMM) is a fundamental operation in deep learning (DL). With DL moving increasingly toward low precision, recent works have proposed novel unary G EMM designs ...

MIT engineers use heat-conducting silicon microstructures to perform matrix multiplication with >99% accuracy hinting at ...

The heat your devices produce could do the computing. A silicon structures turn waste heat into calculations, cutting energy ...