>
JPMorgan CEO Dimon says US economy is booming
Divide and Conquer: The Government's Propaganda of Fear and Fake News
This Political Ad Is So Ridiculous You'll Think It's Satire
"He Knows too Much" - Suge Knight on Diddy's Knowledge of Music's Darkest Secrets
Blazing bits transmitted 4.5 million times faster than broadband
Scientists Close To Controlling All Genetic Material On Earth
Doodle to reality: World's 1st nuclear fusion-powered electric propulsion drive
Phase-change concrete melts snow and ice without salt or shovels
You Won't Want To Miss THIS During The Total Solar Eclipse (3D Eclipse Timeline And Viewing Tips
China Room Temperature Superconductor Researcher Had Experiments to Refute Critics
5 video games we wanna smell, now that it's kinda possible with GameScent
Unpowered cargo gliders on tow ropes promise 65% cheaper air freight
Wyoming A Finalist For Factory To Build Portable Micro-Nuclear Plants
Scientists at DGIST in Korea, and UC Irvine and UC San Diego in the US, have developed a computer architecture that processes unsupervised machine learning algorithms faster, while consuming significantly less energy than state-of-the-art graphics processing units. The key is processing data where it is stored in computer memory and in an all-digital format. The researchers presented the new architecture, called DUAL.
Scientists have been looking into processing in-memory (PIM) approaches. But most PIM architectures are analog-based and require analog-to-digital and digital-to-analog converters, which take up a huge amount of the computer chip power and area. They also work better with supervised machine learning, which includes labeled datasets to help train the algorithm.
To overcome these issues, Kim and his colleagues developed DUAL, which stands for digital-based unsupervised learning acceleration. DUAL enables computations on digital data stored inside a computer memory. It works by mapping all the data points into high-dimensional space; imagine data points stored in many locations within the human brain.
The scientists found DUAL efficiently speeds up many different clustering algorithms, using a wide range of large-scale datasets, and significantly improves energy efficiency compared to a state-of-the-art graphics processing unit. The researchers believe this is the first digital-based PIM architecture that can accelerate unsupervised machine learning.
It is not clear if there are improvements with the DUAL architecture that would improve the CEREBRAS wafer scale chips. The CEREBRAS wafer-scale AI chips have 18 gigabytes of on chip memory. Those would be the ideal way to implement superior processing in memory.