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designing custom and embedded Artificial Intelligence HardWare architectures (AI-HW) to support energy-intensive data movement, speed of computation, and large memory resources that AI requires to achieve its
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an important role in combining information from differently specialized modules to perform more complex tasks. In artificial networks, recent studies demonstrated that modular architectures could lead to
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., Ng, C. J., & Lee, C. C. (2017, October). Embedding stacked bottleneck vocal features in a LSTM architecture for automatic pain level classification during emergency triage. In 2017 Seventh
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optimize the training and inference of modern deep learning architectures. Potential applications will include, but not be limited to, computer vision, natural language processing, climate, etc. References
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-tail cyclic peptides for targeting defined sites in proteins of interest. To achieve this objective, we propose the development of a deep learning architecture. Within the aims of the project, we will