MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more ...

Abstract: In this paper, we propose an over-the-air (OTA)-based approach for distributed matrix-vector multiplications in the context of distributed machine learning (DML). Thanks to OTA computation, ...

Researchers create a photochromic fluorescent system that performs optical neural computing and visual output in one step, cutting power use and complexity. (Nanowerk News) The rapid growth of ...

SANTA CLARA, Calif.--(BUSINESS WIRE)--Further expanding SiFive’s lead in RISC-V AI IP, the company today launched its 2nd Generation Intelligence™ family, featuring five new RISC-V-based products ...

import glsl; [shader("fragment")] void fragment_main() { mat4 matrix = mat4(1.0); vec4 vector = vec4(1.0); vec4 result0 = matrix * vector; vec4 result1 = matrix ...

The growing imbalance between the amount of data that needs to be processed to train large language models (LLMs) and the inability to move that data back and forth fast enough between memories and ...

Abstract: Distributed matrix-vector multiplication plays a key role in numerous computing-intensive applications, including machine learning, by leveraging distributed computing resources known as ...

A new technical paper titled “Leveraging ASIC AI Chips for Homomorphic Encryption” was published by researchers at Georgia Tech, MIT, Google and Cornell University. “Cloud-based services are making ...

Since homomorphic encryption enables SIMD operations by packing multiple values into a vector of operations and enabling pairwise addition or multiplication operations, one (old) conventional method ...

Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...