Greetings! Welcome to the IAP Newsletter with recent and upcoming research publications, news and events. Research includes applications and infrastructure for AI and machine learning, hardware acceleration, operating systems, networking, security, storage and data management - all in the context of distributed systems.
IAP MONTHLY WEBINAR SERIES: TOPICS IN AI
Join our new Series of webinars featuring topics in AI presented by prominent experts in machine learning.
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September 29, 2020, 11am-12pm PT
Prof. Song Han, MIT
“Once-for-All” DNNs: Simplifying Design of Efficient Models for Diverse Hardware
Song's research focuses on efficient deep learning computing. He proposed the “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.
Song completed his Ph.D. in Electrical Engineering at Stanford in 2017, advised by Prof. Bill Dally. He has received numerous awards and was named “35 Innovators Under 35” by MIT Technology Review.
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SAVE the DATE: 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
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.
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SAVE the DATE: January 28, 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.
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IAP WORKSHOPS
The UW Cloud Workshops are being co-organized by Prof. Arvind Krishnamurthy and the IAP for Friday October 30 and Friday February 26.
The focus in October on will be on Programmable Device Hardware Implementations and Applications - including SmartNICs, network-attached storage, FPGA's, etc.
Expect a full day of talks by leading experts in academia and industry. working in AI, hardware acceleration, networking, Big Data, security, storage and data management, all in the context of distributed systems.
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Prof. Arvind Krishnamurthy presents at the 2017 UW Cloud Workshop. |
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SAVE the DATE: UNIVERSITY of WASHINGTON WORKSHOP ON THE FUTURE OF CLOUD COMPUTING, Friday February 26, 2021
The focus of the February workshop will be on AI Implementations and Applications: ML architecture, systems, and programming environments.
Apache TVM and other leading machine learning solutions were (and are being) developed at UW with widespread support from the community.
Hear from faculty and students from interdisciplinary research groups such as SAMPL (System, Architecture, Machine learning, and Programming languages), SYSLab (Computer Systems Lab), MODE (Machine learning, Optimization, Distributed systems, and Estadística), and PLSE (Programming Languages and Software Engineering), and SAMPA.
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SELECT CONFERENCES and PUBLICATIONS in 2H 2020
USENIX ATC, July 15-17, 2020
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A Public Option for the Core, Y. Harchol, D. Bergemann, N. Feamster, E. Friedman, A. Krishnamurthy, A. Panda, S. Ratnasamy, M. Schapira, S. Shenker
Composing Dataplane Programs with μP4, Hardik Soni (Cornell University) Myriana Rifai (Nokia Bell Labs) Praveen Kumar (Cornell University) Ryan Doenges (Cornell University) Nate Foster (Cornell University)
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HOT CHIPS, Santa Clara, CA, August 16-18, 2020
Chipyard – Integrated Design, Simulation, and Implementation of Custom RISC-V SoCs, Alon Amid, Abraham Gonzalez, David Biancolin, Daniel Grubb, Sagar Karandikar, Harrison Liew, Albert Magyar, Howard Mao, Albert Ou, Nathan Pemberton, Paul Rigge, Colin Schmidt, John Wright, Jerry Zhao, Yakun Sophia Shao, Krste Asanovic and Borivoje Nikolic; UC Berkeley
SAINT-S – 3D SRAM Stacking Solution Based on TSV Technology, Kyoungsun Cho, Jinhong Park, Billy Koo, Sunkyoung Seo, Yoonjae Hwang, Sungcheol Park and Mijung Noh; Samsung
If Only We Could Control Them: Challenges and Solutions in Scaling the Control Interface of a Quantum Computer, David Reilly, Microsoft
Applications and Challenges with Near-term Quantum Hardware, Jarrod McClean, Google
Underneath the Hood of a Superconducting Qubit Quantum Computer, Matthias Steffen and Oliver Dial, IBM
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Leveraging Application Classes to Save Power in Highly-Utilized Data Centers
Kostis Kaffes(Stanford University); Dragos Sbirlea, Yiyan Lin, David Lo(Google); Christos Kozyrakis(Stanford University, Google)
High availability in cheap distributed key value storage
Thomas Kim, Daniel L.-K. Wong, Gregory R. Ganger(Carnegie Mellon University); Michael Kaminsky(BrdgAI / Carnegie Mellon University); David G. Andersen(Carnegie Mellon University)
Janus: Latency-Aware Provisioning and Scaling for Prediction Serving Pipelines
Daniel Crankshaw(Microsoft Research); Gur-Eyal Sela(UC Berkeley); Xiangxi Mo(UC Berkeley, Anyscale); Corey Zumar(DataBricks); Ion Stoica, Joseph Gonzalez(UC Berkeley); Alexey Tumanov(Georgia Tech)
Photons: Lambdas on a diet
Vojislav Dukic, Rodrigo Bruno(ETH Zurich); Ankit Singla(ETH Zürich); Gustavo Alonso(ETH Zurich)
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Below, at FMS 2019, Stanford PhD student Tushar Swarmy describes his research on intelligent data planes to Kin Yip Liu, Senior Director of Architecture at Intel. Tushar is advised by Prof. Kunle Olukotun. Tushar also presented at the IAP Stanford-UCSC Cloud Workshop in December 2018.
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KEYNOTE: Time to Put Up or Shut Up: Advancing Security by Creation, not Criticism, Dave Patterson
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BLOGS
Hacking Distributed is a blog hosted by Prof. Emin Gün Sirer for "everyday techies building real systems people use, and their still-with-it-and-technical CTOs".
AWARDS
2020 Maurice Wilkes Award
The award, which is named in honor of the pioneering British computer scientist who built the first operational stored-program computer, recognizes an outstanding contribution by a member of the computer architecture field within the first two decades of their professional career. Ceze and Strauss are the first recipients to share the award in its 22 year history.
PROJECTS
Enzian is a research computer designed by ETH Zurich for computer systems software research, rather than any particular commercial workload. An Enzian node has a big server-class Marvell ThunderX CPU closely coupled in cache coherence to a large Xilinx FPGA, with ample main memory and network bandwidth on both sides. For more info, see the Enzian updates and recent paper at the 2020 Conference on Innovative Data Systems Research (CIDR), Amsterdam - Tackling Hardware/Software co-design from a database perspective
MILESTONES
NEWS ITEMS
September 13, 2020
August 25, 2020
August 18, 2020
August 3, 2020
July 13, 2020
July 10, 2020
June 23, 2020
June 16, 2020
April 17, 2020
IAP Workshop Testimonials
Professor Christos Kozyrakis, Stanford - “As a starting point, I think of these IAP workshops as ‘Hot Chips meets ISCA’, i.e., an intersection of industry’s newest solutions in hardware (Hot Chips) with academic research in computer architecture (ISCA); but more so, these workshops additionally cover new subsystems and applications, and in a smaller venue where it is easy to discuss ideas and cross-cutting approaches with colleagues.”
Professor Heiner Litz, UC Santa Cruz - "The IAP workshops represent extremely valuable events for all attendees including industry members, students and faculty. On my side, multiple project collaborations and student internships have evolved from these meetings leading to a win-win-win situation for all participants.”
Ana Klimovic, PhD student, Stanford - “I have attended three IAP workshops and I am consistently impressed by the quality of the talks and the breadth of the topics covered. These workshops bring top-tier industry and academia together to discuss cutting-edge research challenges. It is a great opportunity to exchange ideas and get inspiration for new research opportunities."
Nathan Pemberton, PhD student, UC Berkeley - "IAP workshops provide a valuable chance to explore emerging research topics with a focused group of participants, and without all the time/effort of a full-scale conference. Instead of rushing from talk to talk, you can slow down and dive deep into a few topics with experts in the field."
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Best,
Jim Ballingall
Executive Director
Industry-Academia Partnership (IAP)
www.industry-academia.org
jim.ballingall@gmail.com
cel: 408-212-1035
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