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Dependable, Efficient and Intelligent Computing Lab

Led by Dr. Xun Jiao, assistant professor of Electrical and Computer Engineering, the Dependable, Efficient, and Intelligent Computing Lab (DETAIL) uses the software-hardware codesign approach to build intelligent, efficient and dependable computer systems to solve emerging societal challenges such as health and security.

Machine learning methods have shown broad success in various application domains such as personalized health, speech recognition, and natural language processing. Nonetheless, the computationally intensive workloads of machine learning incur high energy consumption on existing computing platforms. We aim to solve this problem using a cross-layer software-hardware codesign approach.

At the software and algorithm level, we reduce the computing workload by optimizing the learning models, reducing redundant parameters, enabling computation reuse, etc. At the hardware level, we design efficient architecture using the latest technology such as FeFET-based memory. We then consider and explore the co-optimization on both software and hardware level to maximize the benefits.

Benefiting from the efficient processing of machine learning methods, we can enable machine learning on the edge. This can greatly expand the possible applications of machine learning to the local ends. We look forward to applying machine learning to Internet-of-Things devices for emerging societal problems, such as:

  • Personalized healthcare
  • Cybersecurity
  • Autonomous driving

Current research includes “Energy-Efficient Neural Networks using Computation Reuse Accelerating Neural Networks with Low-precision Parameters.”



Faculty Director

Xun Jiao


Ph.D. Students

Dongning Ma (2019 Spring - )


Undergraduate Students

Shenda Huang (2019 Spring - )
Xingjiang Wang (2019 Spring - )
Vu Tran (2018 Fall - )


Dr. Xun Jiao

Dr. Xun Jiao, Assistant Professor
Department of Electrical and Computer Engineering