SHF: Small: Energy and Computational Efficient Deep Generative AI Models via Emerging Devices, Circuits, and Architectures

University Of Texas At Arlington, Arlington TX

Investigators

Abstract

Deep generative artificial intelligence (AI) models can learn to reproduce their inputs or the variational versions of their inputs. However, a critical challenge that needs to be addressed is their energy and computational cost. Foundational research in the design, verification, operation, and evaluation of deep generative AI hardware and software through novel approaches in emerging devices, circuits, and architectures is desirable. The energy and computational cost of AI has become a bottleneck for its applications in the real world. Research and education will be integrated through course and lab development. Under-represented and women students will be recruited for this project through the Society of Hispanic Professional Engineers, National Society of Black Engineers, and Society of Woman Engineers. This project targets the development of new generative AI models with simpler designs and architecture than are currently available. A novel path is explored for designing deep-learning hardware accelerators via efforts that span from devices and circuits to architectures and algorithms. The Cellular Neural Network-based realizations for key operations in convolution-based networks is studied, because it allows the bulk of the computation associated with a deep generative AI model to be performed in the analog domain. The development of mixed-signal circuits and architectures that lead to the best deep generative network designs by exploiting unique physics of emerging device technologies is investigated. The project is expected to generate orders of magnitude improvements in energy and delay for deep generative AI models, which will promote their applications and benefit the AI industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.