IEEE Distinguished Lecture The slowing down of Moore’s Law growth has coincided with escalating computational demands from machine learning and artificial intelligence. An emerging trend in computing involves building physics-inspired computers that leverage the intrinsic properties of physical systems for specific domains of applications. Probabilistic computing with probabilistic bits (p-bits) has emerged as a promising candidate in this area, offering an energy-efficient approach to probabilistic algorithms and applications -. Several implementations of p-bits, ranging from standard complementary metal oxide semiconductor (CMOS) technology to nanodevices, have been demonstrated. Among these, the most promising p-bits appear to be based on stochastic magnetic tunnel junctions (sMTJs) . Such sMTJs harness the natural randomness in low-barrier nanomagnets to create energy-efficient and fast fluctuations, up to gigahertz frequencies . In this talk, I will discuss how magnetic p-bits can be combined with conventional CMOS to create hybrid probabilistic-classical computers for various applications. I will provide recent examples of how p-bits are naturally applicable to combinatorial optimization, such as solving the Boolean satisfiability problem , energy-based generative machine learning models like deep Boltzmann machines, and quantum simulation for investigating many-body quantum systems. Through experimentally informed projections for scaled p-bit computers using sMTJs, I will demonstrate how physics-inspired probabilistic computing can lead to graphics-processing-unit-like success stories for a sustainable future in computing. S. Chowdhury, A. Grimaldi, N. A. Aadit, S. Niazi, M. Mohseni, S. Kanai, H. Ohno, S. Fukami, L. Theogarajan, G. Finocchio, S. Datta, K. Y. Camsari, “A Full-Stack View of Probabilistic Computing with p-Bits: Devices, Architectures and Algorithms,” IEEE J. Expl. Solid-State Comp. Dev. Cir. 9, 1-11 (2023). W. A. Borders, A. Z. Pervaiz, S. Fukami, K. Y. Camsari, H. Ohno, S. Datta, “Integer Factorization Using Stochastic Magnetic Tunnel Junctions,” Nature 573, 390-393 (2019). N. A. Aadit, A. Grimaldi, M. Carpentieri, L. Theogarajan, J. M. Martinis, G. Finocchio, K. Y. Camsari, “Massively Parallel Probabilistic Computing with Sparse Ising Machines,” Nature Electronics 5, 460–468 (2022). N. S. Singh, S. Niazi, S. Chowdhury, K. Selcuk, H. Kaneko, K. Kobayashi, S. Kanai, H. Ohno, S. Fukami, K. Y. Camsari, “Hardware Demonstration of Feedforward Stochastic Neural Networks with Fast MTJ-Based p-Bits,” IEEE Int. Electron Dev. Meeting (2023). Speaker(s): Kerem Camsari, Room: 81-1A116, Bldg: 81, NIST 325 Broadway, Boulder, Colorado, United States

Do you still use a landline with rotary dial to make telephone calls? No! You should not be using mid-20th Century leadership practices either! Twentieth century leadership practices are no longer effective. In today's dynamic business landscape, marked by hybrid and remote work, our greatest challenge isn't just managing effectively—it's leading with trust. For 21st century workplaces, the key to success lies in this trust-based leadership. Why? Because trusted employees are committed employees. To enhance retention by up to 65% and increase productivity by as much as 50%, we need to cultivate Engaged Leadership skills alongside traditional management capabilities. How do we cultivate the Engaged Leadership skills which can engender trust? The answer lies in learning and practice. This workshop uses real stories from industry to illustrate the differences between engaged and disengaged leadership practices, between empowered and disempowered teams, and between trusted and untrusted cultures. If you have questions you would like to submit before the presentation, please submit them (https://events.vtools.ieee.org/tego_/event/edit/here.https:/forms.gle/121TeMgMAu2MEJtv7) (https://events.vtools.ieee.org/tego_/event/edit/here.https:/forms.gle/121TeMgMAu2MEJtv7) Speaker(s): Leslie, Virtual: https://events.vtools.ieee.org/m/427394

Ansys Q3D Extractor is a part of the Ansys Electronic Desktop that is designed for RLCG extraction under quasi-static assumptions. It has been under continuous development for the last three decades, both as a series of incremental changes as well as technological leaps. Traditionally, it has been used in extracting parasitic (undesirable) coupling between various parts of a structure, such as a PCB or a package, for signal integrity analysis. Q3D Extractor generates accurate RLCG (circuit) models that can be consumed by other tools such as SPICE. In recent years, its capabilities have been extended for modeling touch screens, such as those found in modern phones, and power electronics circuits such as those found in automobiles. Q3D Extractor primarily relies on three different solvers: a DC resistance/inductance solver that uses a finite element method (FEM), a capacitance/conductance solver that uses a fast surface integral equation solver and an AC resistance/inductance solver that also uses a surface integral equation method. Both the CG and ACRL solvers use high performance, parallelized, fast solvers for handling large problems. Sanjay will present an overview of its capabilities, applications and underlying technologies in this presentation. Please note the parking map Agenda: 6:00 pm Doors Open 6:30 pm Online Broadcast starts, welcome-Kris Waage 6:40 pm Did'ja Hear? See description below-Scott Evans 7:00 pm Main Presentation-Sanjay Velamparambil 8:00 End Advanced Energy Inc, 1625 Sharp Point Drive, Fort Collins, Colorado, United States, 80525