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.


Join our new Series of webinars featuring topics in AI presented by prominent experts in machine learning.
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.

Pre-registration is required. Please see the abstract, bio and registration page.
Song Receives the Best Poster Award at the Stanford Cloud Workshop in 2016
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. 
Prof. Carole-Jean Wu presents at the 2019 Cornell Cloud Workshop.
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. 
Christina Receives the Best Poster Award at Stanford Cloud Workshop in 2013


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.
Prof. Arvind Krishnamurthy presents at the 2017 UW Cloud Workshop.

The focus of the February workshop will be on AI Implementations and ApplicationsML 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.

USENIX ATC, July 15-17, 2020

Twizzler: a Data-Centric OS for Non-Volatile Memory, Daniel Bittman and Peter Alvaro, UC Santa Cruz; Pankaj Mehra, IEEE Member; Darrell D. E. Long, UC Santa Cruz; Ethan L. Miller, UC Santa Cruz / Pure Storage (Best Presentation Award Winner!)

PinK: High-speed In-storage Key-value Store with Bounded Tails, Junsu Im and Jinwook Bae, DGIST; Chanwoo Chung and Arvind, Massachusetts Institute of Technology; Sungjin Lee, DGIST (Awarded Best Paper!)

The Future of the Past: Challenges in Archival Storage, Ethan L. Miller, University of California, Santa Cruz, and Pure Storage

End the Senseless Killing: Improving Memory Management for Mobile Operating Systems, Niel Lebeck, Arvind Krishnamurthy, and Henry M. Levy, University of Washington; Irene Zhang, Microsoft Research

POSH: A Data-Aware Shell, Deepti Raghavan, Sadjad Fouladi, Philip Levis, and Matei Zaharia, Stanford University

OPTIMUSCLOUD: Heterogeneous Configuration Optimization for Distributed Databases in the Cloud Ashraf Mahgoub and Alexander Michaelson Medoff, Purdue University; Rakesh Kumar, Microsoft; Subrata Mitra, Adobe Research; Ana Klimovic, Google Research;Somali Chaterji and Saurabh Bagchi, Purdue University
SIGCOMM 2020, New York City, August 10-14, 2020

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)

BeauCoup: Answering Many Network Traffic Queries, One Memory Update at a Time, Xiaoqi Chen (Princeton University) Shir Landau-Feibish (Princeton University) Mark Braverman (Princeton University) Jennifer Rexford (Princeton University)
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

Xilinx Versal Premium Series. Martin Voogel, Yohan Frans and Matt Ouellette, Xilinx

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
ACM Symposium on Cloud Computing 2020 (SoCC '20), Renton, Washington, October 19-21, 2020

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)
FLASH MEMORY SUMMIT (FMS 2020), Santa Clara, CA, November 10-12, 2020

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.
ACM SIGPLAN Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH) with Object-Oriented Programming, Systems, Languages & Applications (OOPSLA), Chicago, Illinois, November 15-20, 2020
December 3-4, 2020

KEYNOTE: Time to Put Up or Shut Up: Advancing Security by Creation, not Criticism, Dave Patterson

RISC-V SUMMIT, San Jose , CA, December 7-10, 2020

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".

A Few Thoughts on Distributed Computing Prof. Ken Birman hosts a prolific blog on distributed systems.


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.


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


RISC-V recently celebrated it's 10th Anniversary. Prof. Dave Patterson interviews the founding team.


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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Jim Ballingall
Executive Director
Industry-Academia Partnership (IAP)
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