>
FDA Chief Says No Solid Evidence Supporting Hepatitis B Vaccine At Birth
Evergreen, Colorado: Another Killing Zone in America
Trump Cryptically Writes "Here We Go!" In Reaction To Russia-Poland Drone Incident, Oil Sp
Qatar Says It Reserves Right To Retaliate Against 'Barbaric' Netanyahu
Methylene chloride (CH2Cl?) and acetone (C?H?O) create a powerful paint remover...
Engineer Builds His Own X-Ray After Hospital Charges Him $69K
Researchers create 2D nanomaterials with up to nine metals for extreme conditions
The Evolution of Electric Motors: From Bulky to Lightweight, Efficient Powerhouses
3D-Printing 'Glue Gun' Can Repair Bone Fractures During Surgery Filling-in the Gaps Around..
Kevlar-like EV battery material dissolves after use to recycle itself
Laser connects plane and satellite in breakthrough air-to-space link
Lucid Motors' World-Leading Electric Powertrain Breakdown with Emad Dlala and Eric Bach
Murder, UFOs & Antigravity Tech -- What's Really Happening at Huntsville, Alabama's Space Po
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.