Project Grant 2323819

Award Date 9/15/23
Completion Date 8/31/26
Dollars Obligated $340K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Columbia, MO 65211, USA
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The National Science Foundation (NSF) awarded a $340,000 Project Grant under the Computer and Information Science and Engineering (CISE) program to the University of Missouri System to develop sustainability-aware design methods for approximate deep neural network (DNN) accelerators. The key objectives of this 3-year project are:

  1. Investigating efficient neural architecture search techniques for reliable and sustainable approximate DNN accelerator designs.
  2. Developing fault mitigation and self-repair capabilities for approximate DNN accelerators through techniques like bypass circuitry, retraining, and weight swapping.
  3. Creating a simulation and FPGA demonstration platform to evaluate the sustainability and performance of the approximate DNN accelerators.

The project outcomes, including new theories, tools, benchmarks, and case studies, will be publicly shared with the broader machine learning and cyber-physical systems research communities. Additionally, the project will create new curriculum and hands-on labs for computer and electrical engineering education.

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