IEEE North Jersey Section

Dear All, You are cordially invited to attend the April ExCom meeting at Montclair State University or on Zoom. Looking forward to seeing you all Best Regards Emad Montclair State University, Montclair, New Jersey, United States, Virtual: https://events.vtools.ieee.org/m/476228

The main goal of this CAS seasonal school is to dive deep into the rapidly developing field of in-memory computing with a focus on Artificial intelligence (AI) and cover its cross-layer design challenges from device to algorithms. The IEEE Seasonal School in Circuits and Systems on In-Memory Computing (IMC 2025) offers talks and tutorials by leading researchers from multiple disciplines and prominent universities and promotes student short presentations to demonstrate new research and results, discuss the potential and challenges of the in-memory accelerators, future research needs, and directions, and shape collaborations. First Talk Title (CAS DL Talk): Neuromorphic Computing: Bridging the gap between Nanoelectronics, Neuroscience and Machine Learning While research in designing AI algorithms has attained a stage where such platforms are able to outperform humans at several cognitive tasks, an often-unnoticed cost is the huge computational expenses required for running these algorithms in hardware. Recent explorations have also revealed several algorithmic vulnerabilities of deep learning systems like adversarial susceptibility, lack of explainability, and catastrophic forgetting, to name a few. Brain-inspired neuromorphic computing has the potential to overcome these challenges of current AI systems. This talk reviews recent developments in the domain of neuromorphic computing from my group guided by an overarching system-science perspective with an end-to-end co-design focus from computational neuroscience and machine learning to hardware and applications. From the top-down algorithm side, I will delve into methodologies that treat neuromorphic spiking architectures as continuously evolving dynamical systems, revealing intriguing parallels with the learning dynamics in the brain. The methodologies discussed enable spiking architectures to transition beyond simple vision-related tasks to complex sequence learning problems and large language model (LLM) architectures. Complimentary to this effort, I will also elaborate on a bottom-up perspective of leveraging the intrinsic physics of emerging post-CMOS technologies like ferroelectrics and spintronics to mimic several neuro-synaptic functionalities in novel device structures operated at low terminal voltages. In-Memory computing architectures enabled by such neuromimetic devices have the potential of enabling two to three orders of magnitude energy efficiency in comparison to state-of-the-art CMOS implementations. I will outline several hardware-software co-design strategies to enable variation-aware, robust, self-healing neuromorphic systems. I will conclude my talk with my vision of expanding the scope of neuromorphic computing beyond simple neurons and synapses by forging stronger connections with computational neuroscience, thereby enabling a new generation of brain-inspired computers. Second Talk Title: Towards AI-Native Hardware Design In this talk, I will cover a body of work from NYU on democratizing and supercharging hardware design using modern AI/ML techniques, from design specification to logic synthesis and early-state timing and routing congestion prediction. I will begin by describing Verigen and CL-Verilog, the first specialized LLMs for automated Verilog code generation. To handle more complex designs, we will discuss our recent work on Chain-of-Thought approaches for hierarchical Verilog code generation and agentic frameworks to translate C code to HLS synthesizable C automatically. Next, I will discuss ABC-RL, a state-RL method to optimize logic synthesis, and VerilLoC, an early-stage predictive model to identify code blocks that can cause downstream timing closure issues. I will conclude by presenting my vision to build "end-to-end" foundation models for hardware design. Co-sponsored by: IEEE North Jersey Section Speaker(s): Dr. Abhronil Sengupta, Dr. Siddharth Garg , Agenda: Hybrid Event Event Time: 8:30 AM to 1:00 PM 9:00-9:30 AM Registration and Networking 9:30-9:35 AM Opening Remarks by Dr. Shaahin Angizi, Vice-Chair, IEEE CAS/ED Chapter 9:35-9:40 AM Remark by Dr. Durga Misra, Chair, ECE Dept, NJIT and Chair, IEEE CAS/ED Chapter 9:40-10:00 AM Student Presentations (3-minute) 10:00-11:00 AM Talk I: Dr. Abhronil Sengupta (Penn State University) Title: Neuromorphic Computing: Bridging the gap between Nanoelectronics, Neuroscience and Machine Learning 11:00 AM-12:00 PM Talk II: Dr. Siddharth Garg (New York University) Title: Towards AI-Native Hardware Design 12:00 PM - 12:05 PM Concluding Remarks by Dr. Shaahin Angizi, Vice-Chair, IEEE CAS/ED Chapter 12:05 PM - 1:00 PM Lunch & Networking and Discussion Location: Eberhardt Hall, Room 112, New Jersey Institute of Technology, Newark, NJ, USA Online on Zoom, Link: https://njit-edu.zoom.us/j/96664865749?pwd=pgm7ZY9IaeZGyBFJgdcy1Ey1XtCmlD.1 Meeting ID: 966 6486 5749 Passcode: 660738 All Welcome: There is no fee/charge for attending IEEE technical seminar. You don't have to be an IEEE Member to attend. Refreshments are free for all attendees. Please invite your friends and colleagues to take advantage of this Invited Distinguished Lecture. Room: Room 112, Bldg: Eberhardt, 154 Summit Street, Newark, NJ 07102, NJIT, Newark, New Jersey, United States, 07102, Virtual: https://events.vtools.ieee.org/m/477163

Abstract: The rapid evolution of Artificial Intelligence (AI) in cybersecurity is reshaping how digital threats are detected, mitigated, and prevented. AI-driven security solutions enhance threat intelligence, automate responses, and strengthen cyber defense mechanisms. However, as AI becomes more embedded in cybersecurity frameworks, it raises critical concerns about human rights, privacy, and ethical governance. The use of AI in surveillance, data monitoring, and decision-making has sparked debates about its potential to infringe on fundamental freedoms, leading to questions about accountability, fairness, and transparency. This webinar will explore the intersection of cybersecurity, AI, and human rights, addressing how AI-driven security measures impact digital privacy, freedom of expression, and the ethical responsibilities of organizations and governments. Experts from cybersecurity, law, and ethics will discuss key challenges such as algorithmic bias, the risks of AI-powered surveillance, and the implications of cybersecurity policies on human rights. Additionally, the session will examine regulatory frameworks and best practices to ensure AI technologies are deployed responsibly while upholding democratic values and societal trust. Speaker Bio: Mr. Sheshananda Reddy Kandula is a seasoned Application Security professional with 15 years of experience, currently working at Adobe, where he specializes in securing web, mobile, and API ecosystems. His expertise lies in identifying and mitigating vulnerabilities in alignment with OWASP Top 10 security standards. He holds industry-recognized certifications, including OSWE, OSCP, and CISSP, and has extensive hands-on experience addressing real-world security challenges. Prior to his role at Adobe, he contributed to global security initiatives at Mastercard, leading efforts in vulnerability management and secure software development. Passionate about advancing cybersecurity, Mr. Kandula actively contributes to the security community by sharing insights on secure coding, threat modeling, and application security best practices. His commitment extends to mentorship, technical leadership, and research, fostering a security-first mindset across organizations and professionals. Through his work, he strives to empower security practitioners, promote awareness, and strengthen digital resilience in an evolving threat landscape. Virtual: https://events.vtools.ieee.org/m/470549

Abstract Fog learning is an emerging paradigm for optimizing the orchestration of artificial intelligence services over contemporary network systems. Different from existing distributed techniques such as federated learning, fog learning emphasizes intrinsically in its design the unique node, network, and data properties encountered in today’s fog networks that span computing elements from the edge to the cloud. An important thread of research in fog learning has been on understanding the role that local topologies formed on an ad-hoc basis among proximal groups of heterogeneous computing elements can play in elevating the achievable tradeoff between intelligence quality and resource efficiency. In this talk, I will discuss recent results on the analysis of fog learning processes which give insights into the impact that these topologies, along with other properties such as model characteristics and fog decision parameters, have on global training performance. Additionally, I will discuss the development of adaptive control methodologies that leverage such relationships for jointly optimizing relevant fog learning metrics. Distinguished Lecturer Series: https://www.comsoc.org/membership/distinguished-lecturers Speaker: https://www.comsoc.org/christopher-greg-brinton Co-sponsored by: North Jersey Information Theory Chapter Speaker(s): Chris Brinton, Agenda: 6:30-7:00pm Gather, Refreshments and Introduction 7:00-8:00pm Lecture 8:00-8:30pm Q&A, networking, wrap-up Room: Main Auditorium, Bldg: CAIT (Center For Advanced Infra And Transport) near ECE Bldg - Lot#59, Rutgers University - Busch Campus, 100 Brett Road, Piscataway, New Jersey, United States, 08854-8058

Smart Career Management Joining us is guest speaker Chitra Venkatraman! She is a retired Electrical Engineer who has earned her BE-Electrical (Chennai, India) and MSEE(USA). She started her engineering career in 1981 at Bell Labs (Lucent Technologies/Alcatel-Lucent/Nokia) where she led the Wireless Technology testing until2018. In her current role as the Chair of the pre-university (K-12), Employment Network and Women in Engineering chapters of Institute for Electrical and Electronics Engineers (IEEE), she is passionate about promoting Engineering as a career for K-12 students and offering career management tips to college students and young professionals. She is a STEM volunteer at the local Science Museum and at an organization that serves underserved student groups. Chitra is also an active member American Red Cross – NJ Region that provides assistance to NJ families impacted by Natural disasters and home fires. [] Room: CELS 120, Bldg: CELS, 1 Normal Ave, Montclair, New Jersey, United States, 07043

Presentation Titled "Smart Career Management - Work to build a career not just to pay the bills". Target audience - Students of Montclair State University Co-sponsored by: Craig Polk Speaker(s): , , Montclair State University, Montclair, New Jersey, United States

Special Presentation by Dr. Samson Lasaulce (Khalifa U., UAE) Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, 17 April 2025 @ 12:00 UTC Topic: Large Language Models (LLMs), Optimization, and Game Theory Abstract: In this talk, we will explore the interplay between large language models (LLMs) and optimization. After introducing a use case (consumption power scheduling) for which studying this interplay is fully relevant, we will survey the main approaches in this area, which include pure LLM-based approaches (e.g., to deal with math word problems) and combined approaches. Both limitations and promising solutions will be discussed. Application to radio resource management and to telecommunications more generally will also be addressed. In the last part of the talk, connections between LLMs and game theory will be discussed. Speaker: [] Samson Lasaulce is a Chief Research Scientist with Khalifa University. He is the holder of the TII 6G Chair on Native AI. He is also a CNRS Director of Research with CRAN at Nancy. He has been the holder of the RTE Chair on the "Digital Transformation of Electricity Networks". He has also been a part-time Professor with the Department of Physics at École Polytechnique (France). Before joining CNRS he has been working for five years in private R&D companies (Motorola Labs and Orange Labs). His current research interests lie in distributed networks with a focus on optimization, game theory, and machine learning. The main application areas of his research are wireless networks, energy networks, social networks, and now climate change. Dr Lasaulce has been serving as an editor for several international journals such as the IEEE Transactions. He is the co-author of more than 200 publications, including a dozen of patents and several books such as "Game Theory and Learning for Wireless Networks: Fundamentals and Applications". Dr Lasaulce is also the recipient of several awards such as the Blondel Medal award from the SEE French society.. Co-sponsored by: Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Virtual: https://events.vtools.ieee.org/m/474729

Power systems face escalating risks from aging infrastructure, extreme weather, cyber and physical threats, increased electrification, and shifting energy demands. Recent failures expose these vulnerabilities. In 2021, severe winter storms in Texas froze pipelines and shut down plants: Over 4.5 million people lost power, 246 died, and damages reached $195 billion. Between 2019 and 2023, wildfires and heatwaves in California triggered rolling blackouts. In 2023, winter storms in Quebec knocked out power for over a million. Europe’s 2022-2023 energy shortages, driven by geopolitical tensions, led to blackouts and supply restrictions. These are just a few examples. Cyber and physical attacks continue to threaten power systems. In 2015 and 2016, cyberattacks in Ukraine cut power to over 230,000. Since 2022, multi-pronged attacks have destroyed generation plants, reduced capacity, and forced the grid into emergency protocols. Blackouts are common, exposing the vulnerability of centralized systems during conflict. Failures happen fast. Recovery is slow. Resilience requires decisive action. Modernizing grids with smart technologies can reduce outage durations by 20% (EPRI, 2024). Decentralizing through microgrids adds redundancy—by 2025, 15% of urban areas will rely on them (IEA, 2025). Predictive maintenance using AI has cut transformer downtime from months to less than a week (DOE, 2024). AI-driven cybersecurity has reduced threat response times by up to 70% (DHS, 2025). Energy storage systems help balance supply and demand, particularly during peak loads, while advanced demand response systems increase grid flexibility and reduce stress during surges. However, resilience is not only about technology. It requires robust supply chains for critical components like transformers, semiconductors, and storage technologies. It depends on understanding the interdependencies between power, water, transportation, and telecommunications systems, where a failure in one sector can cascade into others. Investment strategies must prioritize scalable, climate-adaptive infrastructure while ensuring equitable access for underserved communities. Public-private partnerships will be essential to fund and drive these transformations, while policy frameworks must incentivize innovation, sustainability, and resilience. Data integration and AI will be central to optimizing grid efficiency, identifying vulnerabilities, and guiding proactive interventions. Global benchmarking can also provide insights from regions advancing resilience—lessons that can be applied to diverse infrastructure contexts. For IEEE Young Professionals, the challenge is to design, implement, and advocate for these solutions. It means advancing technical expertise, engaging with policymakers, and promoting investments that ensure sector resilience. This session will present real-world examples, data-driven strategies, and practical frameworks for strengthening power infrastructure resilience. It will outline steps to build robust, adaptive systems across interdependent sectors, regions, nations, and global networks. Speaker(s): Dr. Massoud Amin Agenda: - Introduction (5 minutes) - Key Note by Dr. Masood Amin - (45 minutes) - Q&A (10 minutes) Virtual: https://events.vtools.ieee.org/m/478409

Distributed Machine Learning for FPGAs in the Cloud Machine Learning (ML) is a growing area in both research and applications. Trends include larger and larger ML models and the interest in getting results from ML with low latency and high throughput. To address these trends, researchers are increasingly looking at accelerators (such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), especially those that are directly connected to the network to achieve low latency access to data. In this talk, I will introduce the Open Cloud Testbed (OCT): https://octestbed.org/ OCT is available to researchers who are interested in conducting cloud research with accelerators. We provide GPUS, FPGAs, and AI engines from AMD. The FPGAs and AI engines are directly connected to the network. I will discuss experiments on using OCT for distributed ML using multiple network connected FPGAs. Specifically I will present results for running Resnet50 inference on the imagenet dataset. No hardware knowledge is assumed for this webinar. Speaker(s): Miriam Virtual: https://events.vtools.ieee.org/m/473027