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: Eberhardt 112, Bldg: ECEC Building, 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
Special Presentation by Dr. Samson Lasulce (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
