Tech Talk M Shafique Tinyml Edgeai A Cross Layer Approach For Energy Efficiency And Robustness

Tinyml And Edgeai Systems A Cross Layer Approach With Software And This talk will present design challenges and cross layer frameworks for building highly energy efficient and robust cognitive systems for the tinyml and edgeai applications, which. “energy efficiency and security for tinyml and edgeai: a cross layer approach” dr. muhammad shafique – professor, new york university abu dhabi february 1, 2022.

Energy Efficiency And Robustness Of Advanced Machine Learning Architec However, these dnns require huge processing, memory, and energy costs, thereby posing gigantic challenges on building energy efficient tinyml and edgeai solutions for a wide range of applications from smart cyber physical systems (cps) and internet of thing (iot) domains on resource energyconstrained devices subjected to unpredictable and harsh. Modern machine learning (ml) approaches like deep neural networks (dnns) have shown tremendous improvement over the past years to achieve a significantly high accuracy for a certain set of tasks, like image classification, object detection, natural language processing, and medical data analytics. Join us on june 30 for muhammad shafique’s guest lecture, “a cross layer approach to energy efficient and secure edgeai”!. In my ebrain lab at new york university (ad, us), i have been extensively investigating the foundations for the next generation energy efficient, dependable and secure ai ml computing systems, while addressing the above mentioned challenges across different layers of the hardware and software stacks.

Di Wu On Linkedin Tinyml Edgeai Deeplearning Join us on june 30 for muhammad shafique’s guest lecture, “a cross layer approach to energy efficient and secure edgeai”!. In my ebrain lab at new york university (ad, us), i have been extensively investigating the foundations for the next generation energy efficient, dependable and secure ai ml computing systems, while addressing the above mentioned challenges across different layers of the hardware and software stacks. In his tech talk, prof. shafique will present design challenges and cross layer frameworks for building highly energy efficient and robust cognitive systems for the tinyml and. His research has a special focus on cross layer analysis, modeling, design, and optimization of computing and memory systems. the researched technologies and tools are deployed in application use cases from internet of things (iot), smart cyber physical systems (cps), and ict for development (ict4d) domains. Towards energy efficient and secure edge ai: a cross layer framework to appear at the 40th ieee acm international conference on computer aided design (iccad), november 2021, virtual event. This talk will present design challenges, advanced techniques and cross layer frameworks for building highly energy efficient and robust cognitive systems for the tinyml and edgeai applications, which jointly leverage optimizations at different layers of the software and hardware stacks, and at different design stages (e.g., design time vs. run.