The first part of this talk will focus on cross-layer modeling and design of SOT-MRAM chips based on a comprehensive set of experimentally validated physical models for nanoscale SOT devices and physical design of memory cells, subarrays, peripheral circuits, memory controllers, and the full chip. At the device level, tradeoffs among the write current, error rate, and time will be quantified and will be used to design and optimize memory sub-arrays and to perform DTCO for the entire memory chip based on place and route (PnR). The second part of the talk will present the design and benchmarking of SOT-MRAM content-addressable-memories (CAM) for nearest neighbor search and will show how BEOL compatible Transition Metal Dichalcogenide (TMD) Thin-Film Resistors can be used to significantly improve the resolution of CAMs.

Next-generation computing architectures will have to confront the demise of scaling laws and the unabated increase in AI workloads. Against this backdrop, Compute Memories (CMs) are especially promising, since they drastically reduce ever-more costly data movements, while offering massive parallelism.  Nonetheless, the development of CMs is hampered by the paucity of exploration frameworks for investigating hardware/software co-designed solutions. In this talk, I illustrate two complementary approaches which addresses this challenge, based on open hardware and system simulation frameworks, respectively. The talk also details the architecture of domain-specific CMs for AI using such strategies, each resulting in >100X performance increase compared to traditional processor-centric execution. I will highlight differences in capabilities, target scenarios and implementation philosophies.

Modern computing systems have reached unprecedented levels of complexity, rendering traditional design methodologies increasingly inadequate. As system architectures evolve toward greater specialization and heterogeneity, the challenge intensifies, particularly with the rise of domain-specific architectures that demand intricate optimization across multiple design parameters. This complexity explosion necessitates fundamentally new approaches to system design and optimization. Artificial intelligence agents have demonstrated transformative potential across diverse fields, from autonomous systems to scientific discovery, offering data-driven methodologies that can navigate complex decision spaces. These agents, powered by deep learning and reinforcement learning, have shown remarkable capabilities in domains requiring continuous adaptation and intelligent decision-making. The next frontier is to harness similar agent-based approaches for architectural design and optimization, potentially revolutionizing how we approach memory controller optimization, resource allocation, compiler tuning, and power management. While current ML-assisted architecture research has produced innovative algorithms and methods that enhance system efficiency through learned embeddings and automated design space exploration, the full potential of autonomous AI agents in system design remains largely untapped. As we stand at the threshold of "Architecture 2.0," a crucial question emerges: What foundational infrastructure must be established to enable AI agents to transform computer system design? This talk examines the essential building blocks for developing AI agent-assisted architecture research through a shared ecosystem. Such infrastructure would provide standardized environments for agent development, training datasets, and unified platforms for reproducible experimentation and comparative analysis. The talk presents a vision for collaborative ecosystem development that addresses the unique challenges of bringing AI agents to systems and architecture research. Through collective effort, we can establish the foundations to transform modern computer system design for the next generation of computing. 
 

Ferroelectric materials are central to low‑power electronics, memory, and nanoscale devices, yet traditional design strategies face challenges from size scaling, depolarizing fields, and competing structural instabilities. This tutorial introduces modern approaches for ferroelectric and hyperferroelectric materials discovery that combine microscopic physical models, symmetry-based design principles, and physics-informed machine learning. We first review emerging mechanisms of polarization, including proper, hybrid improper, and hyperferroelectricity, and highlight how polarization can persist in reduced dimensions through structural coupling, strain, and chemical control. We then present computational workflows that integrate first-principles theory, phenomenological free-energy models, and lightweight ML strategies to rapidly screen candidate materials and predict stability and switching behavior. Central to this approach is the use of decoratypes, a site-based materials taxonomy that enables structure-aware discovery in data-scarce regimes. The talk emphasizes design rules and best practices for accelerating ferroelectric discovery for microelectronic applications while maintaining strong physical insight.