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Purdue University Engineering Frontiers

Data Science in Engineering

The Center for Brain-Inspired Computing will lead research in future applications of artificial intelligence in autonomous systems.

Amazon’s Alexa. Google’s Nest. Facebook feeds. Smartphones. The principles of artificial intelligence are seamlessly woven into our daily lives, but we have only scratched the tip of the iceberg.

What if the game-changing technology could be leveraged in a much broader range of applications that could vary from personal assistants to swarms of drones? This is the essential premise that forms the foundation for the Center for Brain-Inspired Computing (C-BRIC), a five-year project supported by $31 million in funding from the Semiconductor Research Corporation’s JUMP program. This program is supported by both industry powerhouses, such as IBM and Intel, as well as U.S. federal funding through DARPA.

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At PurPL, which combines PL research from Purdue’s ECE and CS departments, Milind Kulkarni and his associates explore ways to keep up with increasingly complex software and a profusion of new types of processors.

Purdue University has long been a leader in programming languages and compilers, but its leadership role is sometimes overlooked given that the research is divided between the Computer Science and Electrical and Computer Engineering (ECE) schools. To better showcase its PL skills and improve collaboration, the University created an umbrella group called the Purdue University Programming Languages Group (PurPL). The group unites the leading minds in programming research from both schools, spanning research into PL theory, design, and implementation, as well as language-based security, compiler optimizations, verification, and program synthesis.

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Purdue researchers are making mobile devices smarter and improving the efficiency of machine-learning technologies.

“It analyzes the scene and puts tags on everything,” says Eugenio Culurciello, associate professor of biomedical engineering with appointments in electrical and computer engineering, mechanical engineering, and health and human sciences.

The innovation could find applications in “augmented reality” technologies like Google Glass, facial recognition systems and autonomous cars. “When you give vision to machines, the sky’s the limit,” Culurciello says. Internet companies are using deep-learning software, which allows users to search the Web for pictures and video that have been tagged with keywords. Such tagging, however, is not possible for portable devices and home computers. “The deep-learning algorithms that can tag video and images require a lot of computation, so it hasn’t been possible to do this in mobile devices,” Culurciello says.

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To optimize datacenter resource usage and reduce costs, Yiying Zhang aims to explode the monolithic server into semi-autonomous hardware components orchestrated by a new “LegoOS.”

With the dissipation of Moore’s Law and the rise of machine learning, the tech world has increasingly turned its focus from CPUs to coprocessors, such as GPUs, DSPs, FPGAs and neural net accelerators. At the same time, innovative new technologies continue to expand the possibilities for memory and storage.

Despite high demand for such technologies, integration challenges have slowed their entry into datacenters. “There are a lot of new hardware innovations, such as AI chips, that companies want to embrace, but they usually require that you replace your servers,” says Yiying Zhang, assistant professor of electrical and computer engineering. “Because these new technologies are designed for specific servers, adoption in datacenters has been slow.”

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