Artificial intelligence presents a major challenge to conventional computing architecture. In standard models, memory storage and computing take place in different parts of the machine, and data must move from its area of storage to a CPU or GPU for processing.
The problem with this design is that movement takes time. Too much time. You can have the most powerful processing unit on the market, but its performance will be limited as it idles waiting for data, a problem known as the “memory wall” or “bottleneck.”
When computing outperforms memory transfer, latency is unavoidable. These delays become serious problems when dealing with the enormous amounts of data essential for machine learning and AI applications.
A team of researchers from the School of Engineering and Applied Science, in partnership with scientists from Sandia National Laboratories and Brookhaven National Laboratory, has introduced a computing architecture ideal for AI.
Co-led by Deep Jariwala, assistant professor in the Department of Electrical and Systems Engineering (ESE), Troy Olsson, associate professor in ESE, and Xiwen Liu, a Ph.D. candidate in Jarawala’s Device Research and Engineering Laboratory, the research group relied on an approach known as compute-in-memory (CIM).
In CIM architectures, processing and storage occur in the same place, eliminating transfer time as well as minimizing energy consumption. The team’s new CIM design, the subject of a recent study published in Nano Letters, is notable for being completely transistor-free. This design is uniquely attuned to the way that Big Data applications have transformed the nature of computing.
“Even when used in a compute-in-memory architecture, transistors compromise the access time of data,” says Jariwala. “They require a lot of wiring in the overall circuitry of a chip and thus use time, space and energy in excess of what we would want for AI applications. The beauty of our transistor-free design is that it is simple, small and quick and it requires very little energy.”
This story is by Devorah Fischler. Read more at Penn Engineering Today.
