![]() ![]() “WARF is one of the key differentiators of our school compared to others.” “When I have an idea I don’t have to wait to join Intel and work with thousands of engineers to make it happen,” she says. She says that WARF funding, talented graduate students and access to Catalyst mentors help her stay nimble in a field that is changing faster than at any time in history. “To squeeze the last juice out of silicon, we really have to think how we can offload some of the mission critical applications through hardware-software co-design.” “We can no longer just do transistor scaling and have a better generation of computers,” she says. “When we are hitting the walls – hitting the physical limits – the entire computer and IT industry is no longer driven by Moore’s Law. And the old view – that universities lack the tools to compete with industry – no longer holds true. Li, who worked at IBM for five years before accepting a position at UW-Madison, does not regret her venture into academia. In simplest terms, she is striving “to put more and more computing power into single devices, to make them smarter and more intelligent.” ![]() Li’s research program at UW has a common thread. “We are using FPGA offline to demonstrate that this can be directly transferred to all Cloud providers with no cost,” she says. WARF is funding the prototyping effort, using a versatile ‘DIY’ type of chip called a field-programmable gate array (FPGA) as a platform.įPGA research is hot right now as giants like Microsoft and Amazon look to accelerate the performance of certain critical applications in their data centers. The goal: faster, smarter and more energy-efficient systems for deep learning, with applications like improved speech recognition. With support from the WARF Accelerator Program, her latest project is developing a deep learning accelerator in the Cloud. Immersed in the complex world of neural networks, she has an uncommon ability to see the big picture (“In 10 years maybe we scale an entire data center into one cell phone”), to approach systems holistically (“We have to optimize both algorithm and architecture”) and frame the social implications of research (“Like everything, AI has two sides”). “This does his work collecting data and doing analysis, and sends it to his email 24-7.”Ī conversation with Professor Li – a rising star in the department of electrical and computer engineering – can take many turns. “My student wanted to design something to replace himself.” She gestures to an unimposing circuit board on the bench top. The robot in Jing Li’s laboratory does not look like one, she admits. In the ultracompetitive Wild West of AI, a new generation of pioneers is emerging.Īrmed with the Cloud and a mission to push the limits of deep learning, Jing Li and her team of student “hackers” have bested industry titans and set a performance record. ![]()
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