Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Lightelligence Demo Harnesses Light To Tackle Some Of The Hardest Computational Problems

Company showcases computational power of its fully integrated optical computing platform

Lightelligence, the global optical computing innovator, today revealed its Photonic Arithmetic Computing Engine (PACE), the company’s latest platform to fully integrate photonics and electronics in a small form factor. Leveraging custom 3D packaging and seamless co-design efforts in its technology, Lightelligence remains the world’s only company to demonstrate fully integrated optical computing systems working at speed.

Recommended AI News: Adobe Introduces Creative Cloud Express

PREDICTIONS-SERIES-2022

As Lightelligence’s first demonstration of optical computing for use cases beyond AI and deep learning, PACE efficiently searches for solutions to several of the hardest computational math problems, including the Ising problem, and the graph Max-Cut and Min-Cut problems, illustrating the real-world potential of integrated photonics in advanced computation.

Related Posts
1 of 26,572

“These problems belong to an important class of intractable mathematical problems known as NP-complete, which have stumped mathematicians for the last 50 years,” said Yichen Shen, Ph.D., founder and CEO of Lightelligence. “Algorithms for NP-complete problems are important because they can be mapped to each other, and they have hundreds of practical applications in fields that include cryptography, power grid optimization and advanced image analysis. The progress we’ve made on NP-complete combinatorial optimization problems illustrates the potential of our technology to transform computing.”

Recommended AI News: SynSaber Announces Beta of Saber Technology for Industrial Asset Security

Building on the success of the company’s first-of-its-kind 2019 demonstration of an optical AI system, PACE uses optical matrix multiplication to generate optimal solutions for the Ising problem, and the graph Max-Cut and Min-Cut problems.

The algorithm used by PACE takes advantage of the fundamental principle that performing matrix-vector multiplication with light can be done with extremely low latency, as first announced in Nature Communicationsi by a team of researchers that included Shen. The PACE demo achieves low latency by using a tight loop consisting of repeated optical matrix multiplication and a clever use of controlled noise to generate high-quality solutions to combinatorial optimization problems.

Recommended AI News: Turner Imaging Systems Welcomes Its New Vice President Of Sales & Marketing

[To share your insights with us, please write to sghosh@martechseries.com]

Comments are closed.