Revolutionary computer chips in development at the University of Wisconsin-Madison could make future computers much more efficient and powerful.
With funding from a Defense Advanced Research Projects Agency (DARPA) young investigator award, Jing Li, an assistant professor of electrical and computer engineering at UW-Madison, is creating fully morphable computer chips that can be configured to perform complex calculations, store massive amounts of information within the same integrated unit and perform efficient communication across units.
She has named the new chips “Liquid Silicon.” “Liquid means software and silicon means hardware. It is a collaborative software/hardware technique,” says Li. “You can have a supercomputer in a box if you want. We want to target a lot of very interesting and data-intensive applications, including facial or voice recognition, natural language processing, and graph analytics.”
The chips will pack a powerful computational punch, while being able to store significant amounts of data—tasks that require two entirely different types of hardware in modern computers.
That separation makes our machines less efficient.
“There’s a huge bottleneck when classical computers need to move data between memory and processor,” says Li. “We’re building a unified hardware that can bridge the gap between computation and storage.”
Right now, processor and memory chips are separately produced by different manufacturing foundries owned by different industries. Then, they are assembled together by system engineers on printed circuit boards to make computers and smartphones. The wide separation between computation and storage means that even simple operations, like searches, require multiple steps to accomplish: first fetching data from the memory, then sending that data all the way through the deep storage hierarchy to the processor core.
The chips that Li is developing, by contrast, incorporate memory, computation and communication into the same device using monolithic 3D integration: silicon CMOS circuitry on the bottom connected with solid-state memory arrays on the top using dense metal-to-metal links.
End users will be able to configure the devices to allocate more or fewer resources to memory or computation, depending on what types of applications a system needs to run.
“It can be dynamic and flexible,” says Li. “We originally worried it might be too hard to use because there are too many options. But with proper optimization, anyone can take advantage of the rich flexibility offered by our hardware.”
To help people harness the new chip’s potential, Li’s group also is developing software that translates popular programming languages into the chip’s machine code, a process called compilation.
“If I just handed you something and said, ‘This is a supercomputer in a box,’ you might not be able to use it if the programming interface is too difficult,” says Li. “You cannot imagine people programming in terms of binary zeroes and ones. It would be too painful.”
Thanks to her compilation software, programmers will be able to port their applications directly onto this new type of hardware without changing their coding habits.
To evaluate the performance of prototype liquid silicon chips, Li and her students established an automated testing system they built from scratch. The platform is so versatile that it can reveal reliability problems that even the most advanced industry testing setup typically cannot observe. That’s also why multiple companies recently have sent chips to Li for evaluation.
Given that testing accounts for more than half the consumer cost of computer chips, having such advanced infrastructure at UW-Madison will not only help make liquid silicon chips a reality, but also facilitate future research.
“We can do all types of device-level, circuit-level and system-level testing with our platform,” says Li. “Our industry partners told us that our testing system does the entire job of a test engineer automatically.”
Li is the first computational researcher at UW-Madison ever to receive a DARPA Young Faculty Award. In 2016, she joins 25 other young faculty award recipients nationwide whose research topics range from gene therapy to machine learning. The grant guarantees $500,000 of support for two years.