6 papers
Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration
Leonid Popryho, Ayoub Sadeghi, Inna Partin-Vaisband
High-fidelity TCAD simulation of drift-diffusion transport remains the workhorse of emerging FinFET device design, but it is computationally expensive, especially for 3D structures…
SHIFT: Dynamic Compute Relocation Framework for Communication-Aware Chiplet-Based Systems
Arvin Delavari, Leonid Popryho, Sneha Swaroopa +3
The increasing communication complexity of large-scale heterogeneous systems has motivated runtime methodologies for communication-aware workload placement and routing optimization…
Package-Embedded Coupled Inductor Arrays for High-Performance Computing Power Delivery
Rami Rasheedi, Salma Abdelzaher, Inna Partin-Vaisband
A novel power delivery framework, comprising a package-embedded inductor topology and an inductance-island methodology, is introduced to maximize both inductance and current densit…
PALTO: Physics-Informed Active Learning for Tri-Gate FinFET Design Optimization for Vertical Power Delivery
Ayoub Sadeghi, Leonid Popryho, Inna Partin-Vaisband
This paper demonstrates the effectiveness of machine learning-driven optimization for designing application-specific GaN tri-gate FinFETs in vertical power delivery systems. Conven…
GANGR: GAN-Assisted Scalable and Efficient Global Routing Parallelization
Hadi Khodaei Jooshin, Inna Partin-Vaisband
Global routing is a critical stage in electronic design automation (EDA) that enables early estimation and optimization of the routability of modern integrated circuits with respec…
A Unified Learning Platform for Dynamic Frequency Scaling in Pipelined Processors
Arash Fouman Ajirlou, Inna Partin-Vaisband
A machine learning (ML) design framework is proposed for dynamically adjusting clock frequency based on propagation delay of individual instructions. A Random Forest model is train…