WINLAB Researchers receive NSF Grant for Hardware-accelerated Trustworthy Deep Learning

ECE Professor Yingying Chen (PI) and WINLAB CTO, Ivan Seskar are the recipients of a new NSF planning award titled "Hardware-accelerated Trustworthy Deep Neural Network” from the NSF Principles and Practice of Scalable Systems (PPoSS) program. This is a one-year $250,000 collaborative effort among Rutgers, Indiana University, Temple University and NYIT, covering the disciplines of electrical and computer engineering, computer science, security and data science. The aim of the PPoSS program is to support a community of researchers who will work symbiotically across the multiple disciplines above to perform basic research on the scalability of modern applications, systems, and toolchains. The planning grant will be used to develop a LARGE grant proposal to foster the development of principles that lead to rigorous and reproducible artifacts for the design and implementation of large-scale systems and applications across the full hardware/software stack.

During the planning phase, Yingying’s team will develop a scalable and robust heterogeneous system that includes a new low-cost, secure, deep-learning hardware accelerator architecture and a suite of large data compatible deep learning algorithms. The new technologies resulting from this planning grant will also enable extremely large-scale data and facilitate efficient, low-latency applications in connected vehicles, real-time mobile applications, and timely precision health.
You can find more details on the project at the NSF page here.