>
Big Tech Dominance Ties to Technocracy Agenda...
ITALIAN RESTAURANTS DEMAND "LIBERTÁ" FROM LOCKDOWNS
Peter Thiel bought an $18 million island estate in Miami that was once featured on MTV's...
The New Domestic War on Terror is Coming
Autonomous Flight unveils six-seat eVTOL tricopter air shuttle
Full Autonomous Self Driving Teslas in Vegas Boring Tunnels by the End of 2021
Fish-inspired robots coordinate movements without any outside control
The UK Is Developing Nuclear-Powered Space Exploration for Faster Mars Trips
GM Unveils Cadillac Flying Car For Rich People
Gigafactories With New Solar PV Module Tech Might Cause Solar Glut in 2021
Orbital Assembly Building Parts to Eventually Scale to Large Rotating Space Stations
GM Unveils Cadillac Flying Car For Rich People
How Phoenix Feeds The Hungry With Fresh Food While Saving Local Businesses and Farms
Oak Ridge Research Next Generation Cathode Free Lithium Ion Batteries
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.