Webinars

Join our 2020-2021 series of webinars featuring topics in AI.

Thursday, January 28, 2021, 11am-12pm PT
Prof. Christina Delimitrou, Cornell

Leveraging ML to Design Better Large-Scale Systems

​Christina has received numerous awards for her research at Stanford and Cornell, most recently the 2020 TCCA Young Computer Architect Award. Before joining Cornell, she earned a Ph.D. in Electrical Engineering at Stanford in 2015, where she worked with Prof. Christos Kozyrakis. 

Please check back later for the abstract and registration info.

Below, Christina presents at the 2018 MIT Cloud Workshop.

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Tuesday, September 29, 2020, 11am-12pm PT
Prof. Song Han, MIT Department of Electrical Engineering and Computer Science

“Once-for-All” DNNs: Simplifying Design of Efficient Models for Diverse Hardware Webinar Video

​Abstract: We address the challenging problem of designing deep neural networks that can execute efficiently across a diverse range of hardware platforms, especially in edge devices. Conventional approaches rely on manual design or use automated neural architecture search (NAS) to find a specialized neural network and train it from scratch for each use case, which is computation-ally prohibitive. Last June, researchers released a startling report estimating that using NAS to create a single model resulted in emission of roughly 626k pounds of carbon dioxide. That’s equivalent to nearly five times the lifetime emissions of the average U.S. car, including its manufacturing. I will present a new NAS system for searching and running neural networks efficiently, a once-for-all network (OFA).

By decoupling model training and architecture search, OFA can reduce the carbon emissions resulting from neural architecture search by thousands of times. OFA can produce a surprisingly large number of sub-networks (> 10^19) that can fit different hardware platforms and latency constraints, from cloud GPUs to micro controllers. By exploiting weight sharing and progressive shrinking, the produced model consistently outperforms state-of-the-art NAS methods including MobileNet-v3 and EfficientNet (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1.5x faster than MobileNetV3, 2.6x faster than EfficientNet). In particular, OFA achieves a new state-of-the-art 80.0% ImageNet top-1 accuracy under the mobile setting (<600M MACs). OFA was the winning solution for the 3rdand 4thIEEE Low Power Computer Vision Challenge (LPCVC). OFA has also been applied to efficient video recognition and 3D point cloud. 

Bio: Song Han is an assistant professor in MIT’s Department of Electrical Engineering and Computer Science. He received his PhD degree from Stanford University and bachelor’s degree from Tsinghua University. His research focuses on efficient deep learning computing. He proposed “deep compression” technique that can reduce neural network size by an order of magnitude without losing accuracy, and the hardware implementation “efficient inference engine” that first exploited pruning and weight sparsity in deep learning accelerators. His recent work on hardware-aware neural architecture search was highlighted by MIT News, Qualcomm News, VentureBeat, IEEE Spectrum, integrated in PyTorch and AutoGluon, and received many low-power computer vision contest awards in flagship AI conferences (CVPR’19, ICCV’19 and NeurIPS’19). 

Song received Best Paper awards at ICLR’16 and FPGA’17, Amazon Machine Learning Research Award, SONY Faculty Award, Facebook Faculty Award. Song was named “35 Innovators Under 35” by MIT Technology Review for his contribution on “deep compression” technique that “lets powerful artificial intelligence (AI) programs run more efficiently on low-power mobile devices.” Song received the NSF CAREER Award for “efficient algorithms and hardware for accelerated machine learning”. Below, Song Receives the Best Poster Award at the Stanford Cloud Workshop in 2016.

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Thursday, November 19, 2020, 11am-12pm PT
 Prof. Carole-Jean Wu, Arizona State and Facebook AI Research

Deep Learning: It’s Not All About Recognizing Cats and Dogs 
Webinar Video  

​Abstract: In this webinar, I will talk about the underinvested deep learning personalization and recommendation systems in the overall research community. The training of state-of-the-art industry-scale personalization and recommendation models consumes the highest number of compute cycles among all deep learning use cases. For AI inference, personalization and recommendation consumes even higher compute cycles of 80%. What does state-of-the-art industry-scale neural personalization and recommendation models look like? I will present advancement on the development of deep learning recommender systems, the implications on system and architectural design and parallelism opportunities across the machine learning system stack over a variety of compute platforms. I will conclude with future directions on multi-scale system design and optimization. 
 
Bio: Carole-Jean Wu is a Research Scientist at Facebook AI Research. Her research focus lies in the domain of computer system architecture with particular emphasis on energy- and memory-efficient systems. Her recent research has pivoted into designing systems for machine learning execution at-scale, such as for personalized recommender systems and mobile deployment. Carole-Jean chairs the MLPerf Recommendation Benchmark Advisory Board and co-chairs MLPerf Inference. Carole-Jean holds tenure as an Associate Professor at ASU. She received her M.A. and Ph.D. from Princeton and B.Sc. from Cornell. She is the recipient of the NSF CAREER Award, Facebook AI Infrastructure Mentorship Award, the IEEE Young Engineer of the Year Award, the Science Foundation Arizona Bisgrove Early Career Scholarship, and the Intel PhD Fellowship, among a number of Best Paper awards. She is a senior member of both ACM and IEEE.  
 
Below, Carole-Jean presents “Machine Learning at Scale” at the Cornell Cloud Workshop in 2019.

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