Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...

From ‘Optical Synapses’ to a ‘Photonic Brain’ — Integrated Photonic Neural Networks Toward Low-Power General-Purpose

SHANNON, CLARE, IRELAND, February 5, 2026 /EINPresswire.com/ -- A new publication from Opto-Electronic Technology; DOI ...

Micron's New SSDs Are Gamechangers For AI

SSDs represent a robust growth vector for Micron Technology as memory demands in AI data centers show no signs of stopping.
IEEE

Analyzing the Reliability of Stream Sparse Matrix-Vector Multiplication Accelerators: A High-Level Approach

Abstract: 1 Today, advances in scientific and embedded computing and the incredible proliferation of machine learning algorithms take advantage of specialized hardware accelerators to provide ...
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MIT designs computing component that uses waste heat as a form of information

Proof of concept uses passive components to redirect heat across a chip, allowing temperature patterns to be used for data processing.
IEEE

GAS: General-Purpose In-Memory-Computing Accelerator for Sparse Matrix Multiplication

Abstract: Sparse matrix multiplication is widely used in various practical applications. Different accelerators have been proposed to speed up sparse matrix-dense vector multiplication (SpMV), sparse ...