Aaron Striegel

Our paper, "DRAMScope: Uncovering DRAM Microarchitecture and Characteristics by Issuing Memory Commands," was accepted at ISCA 2024! We had an intriguing journey to figure out the sizes of DRAM MATs, common pitfalls regarding address and data mapping in the computer architecture community, coupled-row activation, and RowHammer/RowPress characteristics induced by the 6F^2 DRAM cell structure, to list a few, by issuing ordinary commands to DRAM. We look forward to presenting our paper in Buenos Aires, Argentina. I am really proud of my students and colleagues, without whom I could not imagine to publish this work in a one-and-done manner. Pdf: https://lnkd.in/gnnQS_XA Authors: Hwayong Nam, Seungmin Baek, Minbok Wi, Michael Jaemin Kim, Jaehyun Park, Chihun Song, Nam Sung Kim, and Jung Ho Ahn Abstract: The demand for precise information on DRAM microarchitectures and error characteristics has surged, driven by the need to explore processing in memory, enhance reliability, and mitigate security vulnerability. Nonetheless, DRAM manufacturers have disclosed only a limited amount of information, making it difficult to find specific information on their DRAM microarchitectures. This paper addresses this gap by presenting more rigorous findings on the microarchitectures of commodity DRAM chips and their impacts on the characteristics of activate-induced bitflips (AIBs), such as RowHammer and RowPress. The previous studies have also attempted to understand the DRAM microarchitectures and associated behaviors, but we have found some of their results to be misled by inaccurate address mapping and internal data swizzling or lack of a deeper understanding of the modern DRAM cell structure. For accurate and efficient reverse engineering, we use three tools: AIBs, retention time test, and RowCopy, which can be cross-validated. With these three tools, we first take a macroscopic view of modern DRAM chips to uncover the size, structure, and operation of their subarrays, memory array tiles (MATs), and rows. Then, we analyze AIB characteristics based on the microscopic view of the DRAM microarchitecture, such as 6F^2 cell layout, through which we rectify misunderstandings regarding AIBs and discover a new data pattern that accelerates AIBs. Lastly, based on our findings at both macroscopic and microscopic levels, we identify previously unknown AIB vulnerabilities and propose a simple yet effective protection solution.

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Our latest research paper, describing an efficient technique to improve the coverage of cellular users in the presence of D2D users, with efficiency established via a rigorous stochastic geometric model. https://lnkd.in/dRGWPJNk

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Applied Affective Computing https://bit.ly/3naKZQw provides State-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches to affective computing systems and social signal processing. Authors: Leimin Tian, Sharon Oviatt, Michal Muszynski, Brent Chamberlain, & Jennifer Healey. #AI #AffectiveComputing #MachineLearning #SocialRobots #BuiltEnvironments #Multimodal #DataCollection #ReinforcementLearning #Synthesizing #Natural and #Believable #EmotionalExpressions ACM, Association for Computing Machinery

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Meet Xiongxiao Xu, an ALCF summer student from Illinois Institute of Technology. Xiongxiao joins us as a Ph.D. candidate studying Computer Science. This summer, he focused on performance-energy trade-offs in heterogenous #HPC systems: “CPU/GPUs in supercomputers consume a great deal of energy at maximum frequency. Decreasing frequency can decrease power consumption, but not necessarily energy consumption, because the execution time will be extended at lower frequency. That is why we call it performance-energy trade-off. Also, the energy consumption optimization in HPC is very significant." Xiongxiao says he enjoyed modeling real-world data from supercomputers, something he would not have had the chance to do in a traditional academic setting. “The experience from Argonne National Laboratory let me know the energetic and healthy work environments at national labs. I also have a chance to talk and connect to world-renowned scientists. It makes me have a deeper understanding in research and inspire my interests of working in academia.” Learn more about student opportunities at the ALCF: https://lnkd.in/ggN2E4Vv

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I am excited to share that our paper, "Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?" has been accepted for publication in IEEE Transactions on Artificial Intelligence (IEEE TAI)! Abstract: In the age of advanced generative AI, distinguishing truth from deception is a growing societal challenge. Our research explores how Large Language Models (LLMs) can aid in detecting misinformation. We use a comprehensive approach, evaluating multiple LLMs with few and zero-shot prompts and incorporating sentiment and emotion annotations to understand their impact on misinformation detection. Additionally, we deliberately distort news using ChatGPT to examine various parameters like abstractness and concreteness, enhancing our framework for identifying authentic, human-generated, and LLM-induced misinformation. Impact Statement: As LLMs evolve, they play a dual role as both defenders and perpetrators of misinformation. This research delves into the conflicting nature of LLMs, evaluating their effectiveness across diverse datasets and prompting techniques. By corrupting authentic news and human-generated fake news with ChatGPT, we quantify parameters like concreteness and abstractness to improve misinformation detection tools. Our findings aim to enhance current frameworks and contribute to the responsible development of AI, fostering a more reliable and resilient information ecosystem in the face of misinformation. Key Contributions: LLM Evaluation: Assessed multiple LLMs' effectiveness in detecting rumor content across six datasets. Prompting Techniques: Investigated few-shot and zero-shot prompting and examined sensitivity to hyperparameters. Sentiment & Emotion: Explored the impact of sentiment and emotion on LLMs' rumor detection capabilities. Dual Corruption Approach: Intentionally distorting both authentic news and human-generated misinformation using ChatGPT. We analyzed parameters such as concreteness and named entity density to gain deeper insights into the impact of these distortions. Explore our research and access the code on GitHub: https://lnkd.in/gbY-9QcT A huge thank you to my co-authors Raghvendra Kumar, Bhargav Goddu, Adam Jatowt, and all those who supported this journey. Here's to advancing the fight against misinformation with cutting-edge AI technologies! #Research #IEEE #ArtificialIntelligence #MisinformationDetection #LargeLanguageModels #AI #FakeNews #IEEE_TAI

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Our special issue in  journal Computer Modeling in Engineering and Sciences (CMES) (SCIE, Q2). "Leveraging AI and ML for QoS Improvement in Intelligent Programmable Networks" is available online https://lnkd.in/ghnyhfWE

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📢 New User Experiment Blog: July 2024 Out Now! 🖥️🔬 Exciting news! Our latest User Experiment blog is now live, featuring groundbreaking research on Multi-Tiered Memory Management by Prof. Dong Li and his team at UC Merced. Highlights: • Innovative approach to managing large, multi-tiered memory systems • Real-world experiment using Chameleon's four-tier memory nodes • Insights on overcoming challenges in HPC memory profiling and migration Dive into the world of high-performance computing and discover how Chameleon is powering cutting-edge research. Read the full blog post here: https://lnkd.in/gzU4EQh5 #ChameleonCloud #UserExperiment #HPC #ResearchComputing

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Welcome to submit your work to this upcoming IEEE/ACM Transactions on Networking (ToN) Special Issue on AI and Networking, which is led by me together with my esteemed colleagues Profs. Ness Shroff, Yingbin Liang, Elisa Bertino, Kaushik Chowdhury, Lei Ying, Kannan Srinivasan, Eylem Ekici, and Sen Lin! This ToN special issue aims to bring together experts from the fields of AI, wired and wireless networking and communications, and security fields to facilitate the dissemination of cutting-edge research findings in the areas of “AI for networks” and “AI on networks,” which will further foster emerging research directions to drive AI and networking innovations forward. Topics of Interest (but not limited to): 1) AI for Networks - AI modeling, methods, and evaluations for edge networks, cloud computing, embedded systems, vehicular networks,airborne networks, satellite networks, and NextG cellular networks; - AI-powered cyber-security for edge networks, cloud computing, embedded systems, vehicular networks,airborne networks, satellite networks, and NextG cellular networks - Explainable AI solutions on and for communications and networking. - AI-assisted network management method design - AI-driven network resource allocation, adaptive modulation, interference management, access control, spectrum auction and sharing, energy efficiency, mobile offloading, and mobile system management. 2) AI on Networks - Decentralized learning, distributed training and inference, federated learning over device-edge-cloud continuum - Trustworthy and privacy-preserving AI over a wide spectrum of networks - Large foundation model pre-training, fine-tuning and inference over large-scale networks - Robust adversarial machine learning over wired and wireless networks - Resource-constrained and edge deployable AI solutions, experiments and testbeds for ML-driven wireless systems. Submission Guidelines Prospective authors should submit their manuscripts following the IEEE/ACM ToN guidelines at https://lnkd.in/gBuyhyZ4. The authors should choose Article Type of “IEEE/ACM Transactions on Networking Special Issue on AI and Networking” in IEEE Author Portal ( the regular TON submission portal), with a cover letter saying the submission is for the special issue according to the following schedule. Important Dates Manuscript Submission Deadline: January 15, 2025 First-round Decision Notification: April 30, 2025 Revised Manuscript Submission: June 30, 2025     Final Decision Notification: August 30, 2025  Final Manuscript Due: September 20, 2025     Planned Publication: December Issue, 2025

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Welcome to submit your work to this upcoming IEEE/ACM Transactions on Networking (ToN) Special Issue on AI and Networking, which is led by me together with my esteemed colleagues Profs. Ness Shroff, Yingbin Liang, Elisa Bertino, Kaushik Chowdhury, Lei Ying, Kannan Srinivasan, Eylem Ekici, and Sen Lin! This ToN special issue aims to bring together experts from the fields of AI, wired and wireless networking and communications, and security fields to facilitate the dissemination of cutting-edge research findings in the areas of “AI for networks” and “AI on networks,” which will further foster emerging research directions to drive AI and networking innovations forward. Topics of Interest (but not limited to): 1) AI for Networks - AI modeling, methods, and evaluations for edge networks, cloud computing, embedded systems, vehicular networks,airborne networks, satellite networks, and NextG cellular networks; - AI-powered cyber-security for edge networks, cloud computing, embedded systems, vehicular networks,airborne networks, satellite networks, and NextG cellular networks - Explainable AI solutions on and for communications and networking. - AI-assisted network management method design - AI-driven network resource allocation, adaptive modulation, interference management, access control, spectrum auction and sharing, energy efficiency, mobile offloading, and mobile system management. 2) AI on Networks - Decentralized learning, distributed training and inference, federated learning over device-edge-cloud continuum - Trustworthy and privacy-preserving AI over a wide spectrum of networks - Large foundation model pre-training, fine-tuning and inference over large-scale networks - Robust adversarial machine learning over wired and wireless networks - Resource-constrained and edge deployable AI solutions, experiments and testbeds for ML-driven wireless systems. Submission Guidelines Prospective authors should submit their manuscripts following the IEEE/ACM ToN guidelines at https://lnkd.in/gBuyhyZ4. The authors should choose Article Type of “IEEE/ACM Transactions on Networking Special Issue on AI and Networking” in IEEE Author Portal ( the regular TON submission portal), with a cover letter saying the submission is for the special issue according to the following schedule. Important Dates Manuscript Submission Deadline: January 15, 2025 First-round Decision Notification: April 30, 2025 Revised Manuscript Submission: June 30, 2025     Final Decision Notification: August 30, 2025  Final Manuscript Due: September 20, 2025     Planned Publication: December Issue, 2025

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【DDL Approaching：15 September】Welcome to submit papers to IEEE Network Series 3 on Quantum Communications and Networking https://lnkd.in/gcgEKyAF Thanks for the great contributions to the previous special issue, Series 1, and 2! https://lnkd.in/g8aHS9xA https://lnkd.in/g34wknvA Quantum communication researchers have achieved significant progress during the recent decades and quantum networking has shown promise in terms of improving the overall functional benefits of the Internet and enabling applications with no counterpart in the classical world. It is a breakthrough technology towards the unimaginable future. In a quantum network, the source and destination may be connected by quantum repeaters/routers for facilitating qubit transmissions. The quantum network of the future is envisaged to pervade the entire globe, relying on terrestrial components, satellites, airplanes, ships and other vehicles. It is anticipated that it will support nearly unconditional security, super-computing power, large network capacity – even at high velocity - and privacy. In the current era, quantum networks are similar to the early stage of the classical Internet in the 1970s. However, they exhibit fundamentally different features, obeying the uncertainty principle, the non-orthogonal indistinguishable theorem, the quantum non-cloning theorem, entanglement and superposition. These constraining features make the design of quantum networks a challenging task. To make the promise of quantum networking a reality, the entire protocol stack spanning from the physical-layer enabling techniques to the application layer requires a dedicated collaborative effort from a large fraction of the engineering and physics community. Topics of interest: • Quantum communication and networking applications • Quantum internet architectures, protocols, and design principles • Intelligent quantum communication networks • Entanglement generation, scheduling, and distribution • Quantum information theory and communication protocol • Quantum memory and repeater design • Quantum routing metrics and new routing algorithms • Congestion control and resource allocation for quantum networks • Quantum data plane and quantum control plane design • Security, privacy, and trust in the context of quantum networks • Quantum network management • Simulators, testbeds, prototypes, implementations, and field experiments • Verification of the existing knowledge in real quantum networks • Performance evaluation and modeling • Migration from classical networks to quantum networks Guest Editors: Ruidong Li, Kanazawa University, Japan Prineha Narang, Havard University, USA Melchior Aelmans, Juniper Networks, Netherlands Peter Mueller, IBM Zurich Research Laboratory, Switzerland Guilu Long, Tsinghua University, China

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A new video from CoCalc by SageMath, Inc. about how to get #julialang running on their Compute Server. https://lnkd.in/gv_WjT5f #computationalmathematics

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Curious about how Large Language Models (LLMs) can enhance Edge Computing and Edge Learning? What security and trustworthiness challenges arise when applying LLMs at the edge? Check out our latest work with Edge Signal titled "𝐋𝐋𝐌-𝐁𝐚𝐬𝐞𝐝 𝐄𝐝𝐠𝐞 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞: 𝐀 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐒𝐮𝐫𝐯𝐞𝐲 𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬, 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬, 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐓𝐫𝐮𝐬𝐭𝐰𝐨𝐫𝐭𝐡𝐢𝐧𝐞𝐬𝐬," now available as an early access article in the IEEE Open Journal of the Communications Society! You can access the full text here: https://lnkd.in/efJCd96g Special congratulations Othmane Friha, for his outstanding dedication and cooperation, and a big congrats to all co-authors—Mohamed Amine Ferrag, Burak Kantarci, Burak Cakmak, Arda Ozgun, and Nassira Ghoualmi-Zine—for their invaluable inputs. A special thanks to Edge Signal for the fruitful collaboration that made this work possible. The work explores the significant potential of LLM-based Edge Intelligence, highlights its diverse applications, and thoroughly investigates security vulnerabilities and defense mechanisms in edge deployments. It also addresses trustworthiness and outlines best practices for developing and deploying secure, responsible systems. #EdgeComputing #EdgeLearning #LLM #Cybersecurity #IEEE

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Our very own Arda Ozgun and Burak Cakmak had the pleasure of collaborating with Othmane Friha, Mohamed Amine Ferrag, Burak Kantarci, and Nassira Ghoualmi-Zine on this paper titled "LLM-Based Edge Intelligence: A Comprehensive Survey on Architectures, Applications, Security and Trustworthiness": https://lnkd.in/efJCd96g. The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a groundbreaking paradigm for intelligent edge devices. With their capacity for human-like language processing and generation, LLMs empower edge computing with a powerful set of tools, paving the way for a new era of decentralized intelligence. Yet, a notable research gap exists in obtaining a thorough comprehension of LLM-based EI architectures, which should incorporate crucial elements such as security, optimization, and responsible development. Our paper aims to bridge this gap by providing a comprehensive resource for both researchers and practitioners. #Edge #EdgeAI #EdgeIntelligence #EdgeSignal #LLM #EdgeComputing #IEEE University of Ottawa uOttawa | Faculty of Engineering | Faculté de génie uOttawa | Kanata North IEEE Technology Innovation Institute Wesley Clover Kanata North Business Association

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When AI Turns Against Us: The Dark Potential of Voice-Enabled Tech Recent findings from UIUC researchers have revealed a troubling side of AI: voice-enabled models can be used to automate scams at just a fraction of the usual cost. Imagine AI agents autonomously executing phone scams, impersonating trusted voices, and targeting millions—all for less than a dollar per attempt. It’s a stark reminder that as AI capabilities grow, so do the ways it can be misused. While companies like OpenAI implement safeguards, this technology is advancing faster than our defenses. What happens when these tools, meant to enhance productivity, fall into the wrong hands? And if this is what’s possible now, what’s next? With threats evolving rapidly, it’s worth considering: Have you considered how AI could work in your favor, not against you?

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Controlling the evolution of a quantum system is broader than quantum computing. We can spatially structure synthetic matter. Now, similarly, add time-domain structure. A new paradigm for quantum phases?

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【DDL：15 August】 Welcome to submit papers to IEEE Network Series 3 on Quantum Communications and Networking https://lnkd.in/gcgEKyAF Thanks for the great contributions to the previous special issue, Series 1, and 2! https://lnkd.in/g8aHS9xA https://lnkd.in/g34wknvA Quantum communication researchers have achieved significant progress during the recent decades and quantum networking has shown promise in terms of improving the overall functional benefits of the Internet and enabling applications with no counterpart in the classical world. It is a breakthrough technology towards the unimaginable future. In a quantum network, the source and destination may be connected by quantum repeaters/routers for facilitating qubit transmissions. The quantum network of the future is envisaged to pervade the entire globe, relying on terrestrial components, satellites, airplanes, ships and other vehicles. It is anticipated that it will support nearly unconditional security, super-computing power, large network capacity – even at high velocity - and privacy. In the current era, quantum networks are similar to the early stage of the classical Internet in the 1970s. However, they exhibit fundamentally different features, obeying the uncertainty principle, the non-orthogonal indistinguishable theorem, the quantum non-cloning theorem, entanglement and superposition. These constraining features make the design of quantum networks a challenging task. To make the promise of quantum networking a reality, the entire protocol stack spanning from the physical-layer enabling techniques to the application layer requires a dedicated collaborative effort from a large fraction of the engineering and physics community. Topics of interest: • Quantum communication and networking applications • Quantum internet architectures, protocols, and design principles • Intelligent quantum communication networks • Entanglement generation, scheduling, and distribution • Quantum information theory and communication protocol • Quantum memory and repeater design • Quantum routing metrics and new routing algorithms • Congestion control and resource allocation for quantum networks • Quantum data plane and quantum control plane design • Security, privacy, and trust in the context of quantum networks • Quantum network management • Simulators, testbeds, prototypes, implementations, and field experiments • Verification of the existing knowledge in real quantum networks • Performance evaluation and modeling • Migration from classical networks to quantum networks Guest Editors: Ruidong Li, Kanazawa University, Japan Prineha Narang, Havard University, USA Melchior Aelmans, Juniper Networks, Netherlands Peter Mueller, IBM Zurich Research Laboratory, Switzerland Guilu Long, Tsinghua University, China

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Our latest work, "Distributed versus Centralized Sensing in Cell-Free Massive MIMO," has been accepted for publication in the IEEE Wireless Communications Letters! 😊 Many thanks to my co-authors Qinglin Zou, Zinat Behdad, Cicek Cavdar! You can find the early access version on https://lnkd.in/dZdEcXXZ In this study, we explore a novel approach to integrated sensing and communication (ISAC) systems in a cell-free massive MIMO setup. We compare traditional centralized sensing with a new distributed approach where partial sensing occurs at the access points (APs). The results show that while the fully-informed distributed approach matches centralized performance, the partly-informed setup achieves significant signaling load reductions.

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