Machine Learning Talks on Campus

29.07.2024
4:00 PM

Efficient Hardware for Neural Networks
Grace Zhang

The last decade has witnessed significant breakthroughs of deep neural networks (DNNs) in many fields. These breakthroughs have been achieved at extremely high computation and memory cost. Accordingly, the increasing complexity of DNNs has led to a quest for efficient hardware platforms. In this talk, class-aware pruning is first presented to reduce the number of multiply-and-accumulate (MAC) operations in DNNs. Class-exclusion early-exit is then examined to reveal the target class before the last layer is reached. To accelerate DNNs, digital accelerators such as systolic array from Google can be used. Such an accelerator is composed of an array of processing elements to efficiently execute MAC operations in parallel. However, such accelerators suffer from high energy consumption. To reduce energy consumption of MAC operations, we select quantized weight values with good power and timing characteristics. To reduce energy consumption incurred by data movement, logic design of neural networks is presented.  In the end, on-going research topics and future research plan will be summarized.

Bio: Grace Li Zhang received the Dr.-Ing. degree from the Technical University of Munich (TUM), in 2018. She joined TU Darmstadt in 2022 as a Tenure Track Assistant Professor. She leads the Hardware for Artificial Intelligence Group. Her research focuses on efficient hardware acceleration for AI algorithms and systems, AI computing with emerging devices, e.g., RRAM and optical components, circuit and system design methodologies for AI, explainability of AI and neuromorphic computing. Three of her papers were nominated as Best Paper Award at Design, Automation and Test in Europe (DATE)  and one of her paper was nominated as Best Paper Award  at Design Automation Conference (DAC). She received Dr.-Wilhelmy-VDI-Preis in Germany and Walter Gademann Preis at TU Munich for outstanding dissertation in 2019.

 

Holger Fröning

NF350, U014.

29.08.2024 
(9:30 AM - 3:30 PM)
30.08.20242024
(9:00 AM - 12:45 PM)

Workshop on Avant-Garde AI Research

Obviously, the development of artificially intelligent systems is not driven by one discipline alone, but rather by an interplay of several different sciences and disciplines. This is why we are delighted to present the Avenues of Intelligence 2024 workshop – a workshop showcasing some of the most intriguing academic branches currently working on artificial intelligence.

Turning the Aesopian fable of the fox and the stork, who served each other food in dishes they couldn't access, on its head, we’ve designed this workshop to ensure that all talks are accessible to a broad academic audience.

We will feature presentations on the latest developments and theories of intelligent systems from multiple perspectives: neuroscience, philosophy, computer science, psychology, engineering, and cognitive science.

Interested? Join us by https://www.kip.uni-heidelberg.de/aoi2024/register for the workshop! And download the workshop program https://antonio.bikic.io/documents/workshop_schedule_aoi_2024.pdf.

Kirchhoff-Institute for Physics (KIP)

European Institute for Neuromorphic Computing 
Im Neuenheimer Feld 225a, 69120 Heidelberg