A Glimpse at Computers of the Future

Swanson hopes to build the second generation of the Moneta storage device in the next six to nine months and says the technology could be ready for market in just a few years as the underlying phase-change memory technology improves. The development has also revealed a new technology challenge.

"We've found that you can build a much faster storage device, but in order to really make use of it, you have to change the software that manages it as well. Storage systems have evolved over the last 40 years to cater to disks, and disks are very, very slow," said Swanson. "Designing storage systems that can fully leverage technologies like PCM requires rethinking almost every aspect of how a computer system's software manages and accesses storage. Moneta gives us a window into the future of what computer storage systems are going to look like, and gives us the opportunity now to rethink how we design computer systems in response."

In addition to Swanson, the Moneta team includes Computer Science and Engineering Professor and Chair Rajesh Gupta, who is also associate director of UC San Diego's California Institute for Telecommunications and Information Technology. Student team members from the Department of Computer Science and Engineering include Ameen Akel, Adrian Caulfield, Todor Mollov, Arup De, and Joel Coburn.

Latest in AI Research

We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define the Gaussian fractional Bethe free energy in terms of the moment parameters of the approximate marginals, derive a lower and an upper bound on the fractional Bethe free energy and establish a necessary condition for the lower bound to be bounded from below. It turns out that the condition is identical to the pairwise normalizability condition, which is known to be a sufficient condition for the convergence of the message passing algorithm. We show that stable fixed points of the Gaussian message passing algorithm are local minima of the Gaussian Bethe free energy. By a counterexample, we disprove the conjecture stating that the unboundedness of the free energy implies the divergence of the message passing algorithm.

Recent progress in neural networks

In the past several years, much progress has been made in neural network technology. Neural networks have been used in many applications of signal processing to classify different sets of patterns. This paper presents some of the trends relevant to application of neural technology. No comprehensive review of the state of the art is attempted. Rather, the emphasis is selectively on certain current trends, as new ideas, that in the author's opinion show promise for the future. However, the reader should note that neural network technology is in a state of flux with several alternative theoretical models and approaches. The paper deals with the practical aspects of the research in neural networks. The basic concepts of neural networks and progress in learning algorithms are briefly reviewed, followed by a discussion of the trends relevant to hardware implementations of these networks. Finally, hybrids comprising neural networks, expert systems, and genetic algorithms are considered.