Advanced AI-Based Techniques Scale-up Solving Complex Combinatorial Optimization Problems

A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more more scalable way than state-of-the-art methods, according to a study led by engineers at the University of California San Diego. In the paper, which was published May 30 in Nature Machine Intelligence, researchers present HypOp, a framework that uses unsupervised learning and hypergraph neural networks.

A new neuromorphic chip for AI on the edge, at a small fraction of the energy and size of today’s compute platforms

An international team of researchers has designed and built a chip that runs computations directly in memory and can run a wide variety of AI applications–all at a fraction of the energy consumed by computing platforms for general-purpose AI computing. The NeuRRAM neuromorphic chip brings AI a step closer to running on a broad range of edge devices, disconnected from the cloud, where they can perform sophisticated cognitive tasks anywhere and anytime without relying on a network connection to a centralized server.

Pushing the Boundaries of Moore’s Law: How Can Extreme UV Light Produce Tiny Microchips?

Some analysts say that the end of Moore’s Law is near, but Patrick Naulleau, the director of Berkeley Lab’s Center for X-Ray Optics (CXRO), says that it could be decades before the modern chip runs out of room for improvement, thanks to advances in materials and instrumentation enabled by the CXRO.