TASChips
Real-time thermal intelligence for AI-era chips
Physics-enforced, reduced-order learning thermal analysis
Real-time chip-level thermal intelligence for AI-era superchip design and data center operations
TASChips (Thermal Analysis of Semiconductor Chips) delivers real time, high-fidelity, chip-level thermal intelligence for AI era superchips, including CPUs, GPUs, AI chips, photonic chips and other advanced processors, at a computational cost orders of magnitude lower than direct numerical methods. Powered by a physics-enforced POD Galerkin projection framework, TASChips enables up to one million× faster for peak temperature prediction in large scale chips, supporting real time thermal monitoring, control, and optimization across chip design and AI data center operations.
Problem
• AI-era GPUs and accelerators operate at extremely high power density and tight thermal margins
• Local hot spots can develop within milliseconds under dynamic workloads
• Thermal throttling directly reduces performance, energy efficiency, and hardware lifespan
• Cooling and reliability costs scale rapidly as chip power and system scale grow
Why current tools fall short
• Direct numerical methods provide high accuracy but are too slow for design iteration and operational use
• Circuit or compact models are fast but cannot capture fine-grained spatial hot spots
• Machine learning models are fast but lack accuracy and physical interpretability with no physics guidance during inference
• None support real-time, physics-accurate thermal feedback across design and operations
Market pull
• AI-era chips operate at extremely high power density, making thermal limits a primary constraint on performance and cost
• Thermal analysis is moving from late-stage verification to continuous use in design and operations
• There is strong demand for real-time, physics-accurate thermal analysis at chip and system scale
Awards and Recognitions
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IEEE ITherm – Prof. Avram Bar-Cohen Best Paper Award
Recognized for outstanding contributions to chip-level thermal analysis using POD-Galerkin based methods.
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Defense Advanced Research Projects Agency (DARPA) Recognition
DARPA highlighted significant recent progress in physics-based compact thermal modeling based on Proper Orthogonal Decomposition that potentially can bridge scales, with targeted 1 °C prediction accuracy and 1,000× computational effort reduction.
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NSF Research Story
TASChips research was featured by the NSF for enabling faster, cooler, and longer-running chips through physics-enforced, reduced-order learning thermal analysis.
Where it plugs in
TASChips can be used as a standalone thermal engine or integrated into chip design flows and data center digital twins for real time thermal insight.
Benefits for chip teams
Faster design iteration, earlier thermal measures, better reliability decisions.
Benefits for AI data centers
Better sustained performance, smarter cooling control, lower failure risk.
Benefits for EDA and simulation
Real time thermal feedback inside existing simulation and design workflows.
NSF Funding
Current and past support for TASChips and related research from National Science Foundation (NSF).
Milestones
Featured publications
Selected peer reviewed papers demonstrating the core methods and impact of TASChips.
Mailing List Registration
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Contact
For demos, business partnerships, licensing, or research collaboration.
Hiring
We are recruiting Ph.D. students to work on NSF-funded research at the intersection of thermal simulation, AI chips, and high-performance computing.
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Ph.D. Position: Reduced-Order Thermal Simulations for GPUs and AI Chips
Focus on physics-based reduced-order thermal modeling, GPU-accelerated simulation, and software development for CPU/GPU/AI-chip thermal analysis. Start date: January or June 2026.
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Ph.D. Position: Thermal-Aware Applications for GPUs and AI Accelerators
Focus on GPU-accelerated parallel simulation, open-source software frameworks, and thermal-aware applications for chip architecture and AI data centers. Start date: January or June 2026.
To apply, email application materials to yuliu@clarkson.edu and mcheng@clarkson.edu.