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Publicado el 26/06/2024

IEEE AR EDS/SSCS - Conferencia: "Efficient Computing for AI and Robotics"
Vivienne Sze - 01 de julio de 2024 - UTN FRBA

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INVITACIÓN
El Capítulo Conjunto Argentino de las Sociedades Técnicas IEEE Electron Devices (EDS) y Solid State Circuits (SSCS), junto con el Departamento de Ingeniería Electrónica de la Universidad Tecnológica Nacional, Facultad Regional Buenos Aires, y con el auspicio del Programa de Visitantes Distinguidos de la SSCS, invitan a la disertación que brindará la Dra. Vivienne Sze sobre "Efficient Computing for AI and Robotics: From Hardware Accelerators to Algorithm Design".
La Dra. Sze  es miembro del Electrical Engineering and Computer Science Department del MIT (USA).

Abstract
The compute demands of AI and robotics continue to rise due to the rapidly growing volume of data to be processed; the increasingly complex algorithms for higher quality of results; and the demands for energy efficiency and real-time performance. In this talk, we will discuss the design of efficient hardware accelerators and the co-design of algorithms and hardware that reduce the energy consumption while delivering real-time and robust performance for applications including deep neural networks, data analytics with sparse tensor algebra, and autonomous navigation.  We will also discuss our recent work that balances flexibility and efficiency for domain-specific accelerators and reduces the cost of analog-to-digital converters for processing-in-memory accelerators. Throughout the talk, we will highlight important design principles, methodologies, and tools that can facilitate an effective design process.

Speaker 

Vivienne Sze, PhD
Vivienne Sze is Professor in the Electrical Engineering and Computer Science Department at MIT. She works on computing systems that enable energy-efficient machine learning, computer vision, and video compression/processing for a wide range of applications, including autonomous navigation, digital health, and the internet of things. 
She is widely recognized for her leading work in these areas and has received awards, including faculty awards from Google, Facebook, and Qualcomm, the Symposium on VLSI Circuits Best Student Paper Award, the IEEE Custom Integrated Circuits Conference Outstanding Invited Paper Award, and the IEEE Micro Top Picks Award.

As a member of the Joint Collaborative Team on Video Coding, she received the Primetime Engineering Emmy Award for the development of the High-Efficiency Video Coding video compression standard.  
She is a co-editor of High Efficiency Video Coding (HEVC): Algorithms and Architectures (Springer, 2014) and co-author of Efficient Processing of Deep Neural Networks (Synthesis Lectures on Computer Architecture, Morgan Claypool, 2020). 
For more information about her research, please visit http://sze.mit.edu. 

* Fecha y Hora: Lunes 01 de Julio de 2024, 04:00 PM a 05:30 PM AR (UTC-03:00) Buenos Aires

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* Lugar: Universidad Tecnológica Nacional, Facultad Regional Buenos Aires, Sede Medrano
Medrano 951, Ciudad de Buenos Aires
Aula 101, Departamento de Electrónica, Primer Piso

* Se solicita Inscripción Previa en https://events.vtools.ieee.org/event/register/424204

* Este es un evento presencial, y la disertación será en idioma inglés.

* Contacto:  Juan Manuel Perdomo 

 

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