ceclub – CE CLUB

Rotem Ben Hur – Enhancing Computer Performance with memristive Memory Processing Units: From General-Purpose Automation to DNA Sequencing Acceleration

Rotem Ben Hur

Enhancing Computer Performance with memristive Memory Processing Units: From General-Purpose Automation to DNA Sequencing Acceleration

Computer systems that facilitate tight integration of data storage and processing can eliminate the “memory wall” and “power wall” bottlenecks. The memristive Memory Processing Unit (mMPU) architecture contains memory cells that can also execute logical functions, such as Memristor-Aided Logic (MAGIC) NOR gates. The mMPU supports both general-purpose computing and application-specific acceleration. This research contributes to both these aspects of the mMPU.

First, we introduce frameworks that automate the mapping of any desired logical function into the mMPU. These frameworks, called SIMPLE and SIMPLER, employ algorithms that optimize any arbitrary in-memory MAGIC operation sequences. Various performance criteria can be configured, including latency (SIMPLE), throughput (SIMPLER), chip area, and energy consumption. Both frameworks provide significant improvements over previous solutions.

Focusing on the specific application of DNA sequencing, we present an mMPU-based accelerator called DART-PIM. Unlike prior studies which suffer from data-transfer bottlenecks, DART-PIM integrates all stages on a single chip for maximal end-to-end performance. This is achieved through a unique data organization technique and a novel algorithmic flow tailored for in-memory implementation. DART-PIM achieves two orders of magnitude improvement in throughput and energy efficiency, compared to state-of-the-art existing architectures.

Rotem is a PhD student supervised by Prof. Shahar Kvatinsky.

Sunday, 30.06, 10:30

Zisapel 608 ; and

https://technion.zoom.us/j/2714110664?omn=92829605892

Eytan Modiano – Optimizing Information Freshness in Wireless Networks: From Theory to Implementation

Optimizing Information Freshness in Wireless Networks: From Theory to Implementation

Eytan Modiano
Laboratory for Information and Decision Systems
Massachusetts Institute of Technology

Age of Information (AoI) is a recently proposed performance metric that captures the freshness of the information from the perspective of the application. AoI measures the time that elapsed from the moment that the most recently received packet was generated to the present time. In this talk, we explore the AoI optimization problem in wireless networks.

We start by considering a wireless network with a number of nodes transmitting information to a base station and develop low-complexity transmission scheduling policies that result in near-optimal AoI performance. We then extend our results to wireless networks under general interference constraints, and develop joint routing and scheduling schemes for minimizing AoI. Finally, we discuss implementation of our transmission scheduling policies using software defined radios, and application to remote tracking of vehicles using UVAs.

Bio:

Eytan Modiano is The Richard C. Maclaurin Professor in the Department of Aeronautics and Astronautics and the Laboratory for Information and Decision Systems (LIDS) at MIT. Prior to Joining the faculty at MIT in 1999, he was a Naval Research Laboratory Fellow between 1987 and 1992, a National Research Council Post Doctoral Fellow during 1992-1993, and a member of the technical staff at MIT Lincoln Laboratory between 1993 and 1999. Eytan Modiano received his B.S. degree in Electrical Engineering and Computer Science from the University of Connecticut at Storrs in 1986 and his M.S. and PhD degrees, both in Electrical Engineering, from the University of Maryland, College Park, MD, in 1989 and 1992 respectively.

His research is on modeling, analysis and design of communication networks and protocols. He received the Infocom Achievement Award (2020) for contributions to the analysis and design of cross-layer resource allocation algorithms for wireless, optical, and satellite networks. He is the co-recipient of the Infocom 2018 Best paper award, the MobiHoc 2018 best paper award, the MobiHoc 2016 best paper award, the Wiopt 2013 best paper award, and the Sigmetrics 2006 best paper award. He was the Editor-in-Chief for IEEE/ACM Transactions on Networking (2017-2020), and served as Associate Editor for IEEE Transactions on Information Theory and IEEE/ACM Transactions on Networking. He was the Technical Program co-chair for IEEE Wiopt 2006, IEEE Infocom 2007, ACM MobiHoc 2007, and DRCN 2015; and general co-chair of Wiopt 2021. He had served on the IEEE Fellows committee in 2014 and 2015, and is a Fellow of the IEEE and an Associate Fellow of the AIAA.

Wednesday, 27.03, 11:30
Meyer 861

Adam J. Aviv – From Dashboards to Labels: Helping users manage and make decision about privacy

Adam J. Aviv

George Washington University

From Dashboards to Labels: Helping users manage and make decision about privacy

The surveillance economy, where tracking and collecting data on uses for the purpose of advertising and other actions, is central to much of the money-making enterprises of the modern technology ecosystem. Due to regulations and other forces, some of the largest companies, such as Google and Apple, have prioritized mechanisms for users to better manage and receive information about the kinds of data that is being collected about them. In this talk, I will explore how effective these mechanisms really are and ask the question, who are they really serving? I will present recent experiments we’ve performed on Google’s data dashboards and their effectiveness, and also present ongoing work on Apple’s app-based privacy nutrition labels, which describe apps functionality with relation to privacy.

Bio:

Adam J. Aviv is an Associate Professor (with tenure) in the Department of Computer Science at the George Washington University and is the director of the GW-Usable Security (GWUSEC) Lab. He is currently on sabbatical during the 2023/2024 academic year and is a visiting scholar in the international school and the department of industrial engineering at Tel Aviv University.

Dr. Aviv has published over 80 peer reviewed papers in areas related to computer security, privacy, and applied cryptography. Currently, his primary academic interests lie at the intersection of human computer interaction (HCI) and computer security and privacy, as well as research in network security and applied cryptography. He has made significant contributions in the space of mobile authentication, studying how users choose passwords and PINs for their mobile devices. Prior to GW, he was a assistant professor at the United States Naval Academy and a visiting assistant professor at Swarthmore College. Dr. Aviv received his B.S.E from Columbia University in the City of New York and his M.S.E. and Ph.D. from the University of Pennsylvania. He is a recipient of six NSF awards as a PI, including the prestigious NSF CAREER award.

Wednesday, 20.03.24
Meyer 861

Video

Alon Rashelbach – Trading Memory Accesses for Computations in Packet Processing and Beyond

Alon Rashelbach

Trading Memory Accesses for Computations in Packet Processing and Beyond

Range matching plays a crucial role in computer systems, including networking, security, and storage. It serves the purpose of locating a range that encompasses a given input number from a vast collection of ranges. Address translators in operating systems and longest-prefix matching in networks heavily rely on range matching. However, existing range matching algorithms are limited in scalability and performance due to their reliance on pointer-chasing techniques.

We introduce a novel data structure called the Range Query Recursive Model Index (RQRMI) to address the challenges associated with range matching. By leveraging shallow neural networks, RQRMI enables the learning of range distributions, transforming expensive lookup operations into efficient neural network inference. By employing the RQRMI model, we achieve an impressive range compression ratio of up to 90X. This compression capability allows for direct lookup operations while fitting within the CPU core cache. Importantly, the RQRMI training algorithm guarantees a strict upper bound on lookup latency, ensures the correctness of results, and exhibits fast convergence rates.

We have developed NuevoMatch, an algorithm for multi-field packet classification that leverages RQRMI models (SIGCOMM’20), and successfully integrated it into the critical path of Open vSwitch, a broadly used virtual switch (NSDI’22). Through the utilization of RQRMI, we have achieved remarkable scalability, enabling Open vSwitch to handle 500 times more routing rules while experiencing a throughput speedup of up to 160 times. Furthermore, our work demonstrates the versatility of RQRMI beyond software. Specifically, we observed up to 20X reduction in the memory footprint required for DNA hardware accelerators (BCB’23) and developed hardware for enhancing the scalability of network packet processors (MICRO’23).

Joint work with Igor DePaula, Ori Rottenstreich, and Mark Silberstein

Alon is a PhD student supervised by Prof. Mark Silberstein and Ori Rottenstreich

Wednesday, 13.09.23, 11:30

Meyer 861