The growing capabilities of sensing, computing and communication devices are leading to an explosion of Internet of Things (IoT) infrastructures. The challenges stem from several major aspects in terms of scalability. First, the number of edge devices is enormous, e.g., in the order of millions or even billions, which makes a centralized management infeasible. Second, there are multiple layers of heterogeneity. Third, mission-critical applications have stringent requirements in correctness, resilience, timeliness, security, and safety. It is difficult for a large-scale IoT system to satisfy these requirements due to the increasing adversarial surfaces. To tackle these challenges, cross-layer and full hardware/software stack solutions are needed for the design and deployment of scalable, secure, and smart mission-critical IoT systems.
This workshop, funded by National Science Foundation (NSF), will assemble a diverse group of researchers with expertise spanning three different computation layers, data centers, gateways/aggregators, and edge devices, and covering research topics including resource management, security & privacy, computer architecture/systems, and algorithms. This workshop will also include lightning talks and poster sessions for students to present their research works.
Agenda
June 03, 2022 (All times in Eastern Time), the Pennsylvania room at the Statler Hotel in Ithaca, hosted by Syracuse University
9:00am – 9:15 am
X. Sharon Hu, University of Notre Dame
9:15am – 10:00am
José F. Martínez, Cornell University, https://martinez.csl.cornell.edu/
Title: PIMCloud: Scaling up Server performance via in-memory processing
Abstract: The slowdown of Moore’s Law, combined with advances in 3D stacking of logic and memory, has pushed architects to revisit the concept of processing-in-memory (PIM) to overcome the memory wall bottleneck. This PIM renaissance finds itself in a very different computing landscape from the one twenty years ago, as more and more computation shifts to the cloud. Most PIM architecture papers still focus on best-effort applications, while PIM’s impact on latency-critical cloud applications is not well understood.
This paper explores how data centers can exploit PIM architectures in the context of latency-critical applications. We adopt a general-purpose cloud server with HBM-based, 3D-stacked logic+memory modules, and study the impact of PIM on six diverse interactive cloud applications. We reveal the previously neglected opportunity that PIM presents to these services, and show the importance of properly managing PIM-related resources to meet the QoS targets of interactive services and maximize resource efficiency. Then, we present PIMCloud, a QoS-aware resource manager designed for cloud systems with PIM allowing colocation of multiple latency-critical and best-effort applications. We show that PIMCloud efficiently manages PIM resources: it (1) improves effective machine utilization by up to 70% and 85% (average 24% and 33%) under 2-app and 3-app mixes, compared to the best state-of-the-art manager; (2) helps latency-critical applications meet QoS; and (3) adapts to varying load patterns.
This work was supported in part by NSF and the Semiconductor Research Corporation (SRC) through the DEEP3M Center, part of the E2CDA program; and by DARPA and SRC through the CRISP Center, part of the JUMP program.
Bio: José Martínez is the Lee Teng-hui Professor and the Senior Associate Dean for Diversity & Academic Affairs in the College of Engineering at Cornell University. He is co-founder and associate director of the DARPA/SRC Center for Research in Intelligent Storage and Processing in Memory (CRISP) and the NSF Science and Technology Center for Research on Programmable Plant Systems (CROPPS). His research has received a number of awards over the years; among them: two IEEE Micro Top Picks papers; a HPCA Best Paper award, as well as MICRO and HPCA Best Paper nominations; a NSF CAREER Award; two IBM Faculty Awards; and a Distinguished Educator Award by the University of Illinois’ Computer Science Department. On the teaching side, he has been recognized with two Kenneth A. Goldman ’71 and one Dorothy and Fred Chau MS’74 College of Engineering teaching awards; a Ruth and Joel Spira Award for Teaching Excellence; thrice as the most influential college educator of a Merrill Presidential Scholar (Andrew Tibbits ’07, Gulnar Mirza ’16, and Angela Jin ’21); and as the student-elected 2011 Tau Beta Pi Professor of the Year in the College of Engineering. José is an IEEE Fellow, an elected member of ACM SIGARCH‘s Board of Directors, and a member of the steering committees of ISCA and MICRO.
10:00am – 10:15am
10:15am – 11:00am
Qinru Qiu, Syracuse University, http://hydrogen.syr.edu/~qqiu/
Title: Neuromorphic Computing for Energy Efficient Near Sensor Adaptive Machine Intelligence
Abstract: The IoTs and edge devices are on the frontier of interacting with the physical world for sensing, perception, and recognition. The limited battery capacity of these devices demands highly energy efficient information representation, computing and communication. The constantly changing environment and mission requirements call for the ability of online learning and adaptation. Inspired by structure and behavior of biological neural systems, spiking neural network (SNN) models and neuromorphic computing hardware adopt many energy efficient features of biological systems. They have been proven to be effective for mobile and edge applications. In this talk I will introduce our works on applying SNNs and neuromorphic computing in processing multivariate time sequences such as sensor readings. Using neurons modeled as a network of infinite impulse response filters, our SNN network can either work as a classifier to detect temporal patterns from the input sequences or as a generator to generate desired temporal sequence. The ability to discern temporal patterns allows us to adopt very sparse input representation, where information is encoded by the intervals between spike events. When coupled with event driven computing and communication, such temporal coding provides significant energy savings. Online learning and domain adaptation of the model will also be discussed in this talk.
Bio: Dr. Qinru Qiu received her PhD in Electrical Engineering from University of Southern California in 2001. She is currently a professor and the director of the graduate program in the Department of Electrical Engineering and Computer Science at Syracuse University. Dr. Qiu has more than 20 years of research experience in machine intelligence and more than 15 years’ experience in neuromorphic computing. She is a recipient of NSF CAREER award in 2009, and IEEE Region 1 Technological Innovation award in 2020. She serves as an associate editor for IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Circuit and Systems Magazine, IEEE Transactions on Cognitive and Developmental Systems, and Frontier on Neuroscience on Neuromorphic Engineering. She has also served as a technical program committee member of many conferences including DAC, ICCAD, ISLPED, DATE, etc. She is the director of the NSF I/UCRC (Industry University Collaborative Research Center) ASIC (Alternative Sustainable and Intelligent Computing) Center Syracuse Site.
1:30pm – 2:00pm
Speaker: Fanxin Kong, Syracuse University
Title: Attack-Resilient Cyber-Physical Systems
2:00pm – 2:30pm
Speaker: Xiaolong Guo, Kansas State University
Title: AST-based RT-Level Hardware Vulnerability Analysis: QIF-Verilog and IF-Tracker
2:30pm –3:00pm
Speaker: Xun Jiao, Villanova University
Title: Robust Computing Against Uncertain Operating Conditions and Data: From Hardware to AI
3:00pm – 3:30pm
Speaker: Song Han, University of Connecticut
Title: Towards the Design of Large-scale Heterogeneous Industrial Internet-of-Things (IIoT) Systems
Organizers
General chair
Fanxin Kong, Syracuse University, https://sites.google.com/site/fanxink/
Principal Investigators
X. Sharon Hu (Lead PI), University of Notre Dame, https://sites.nd.edu/xsharon-hu/
Song Han, University of Connecticut, https://utc-iase.uconn.edu/person/song-han/
Fanxin Kong, Syracuse University, https://sites.google.com/site/fanxink/
Xun Jiao, Villanova University, http://www.ece.villanova.edu/~xjiao/
Xiaolong Guo, Kansas State University, https://ece.k-state.edu//people/faculty/guo/