Thermal Architecture Integrated into Next-Generation Silicon.
We co-design the thermal layer of AI accelerators, aligning heat extraction with real power density through microfluidic integration and impedance engineering.
The Constraint
Heat is not uniform across modern AI dies. Dense compute regions create localized thermal stress that legacy uniform cooling cannot address.
The Problem with Traditional Flow
Traditional cooling is designed after the fact. Without chip-level power mapping and impedance zoning, flow distribution remains uneven.
Cooling today is emergent. Not architected.
The Coeffici Model
Coeffici co-designs thermal architecture at the silicon level, integrating microfluidics into the die layout to match power density maps.
The result is deterministic heat extraction that reduces throttling and improves energy efficiency.
The Architecture Stack
A co-design stack that embeds thermal architecture into the silicon program from floorplan through validation.
Geometry IP
Microchannel placement strategies derived from chip floorplans and power maps.
Impedance Modeling Engine
Thermal impedance networks that align pressure drop with power density.
Multi-Physics Validation
Multi-physics modeling with experimental correlation for silicon accuracy.
Integration Frameworks
Co-design workflows for silicon, package, and cold plate integration.
Validation
Multi-physics modeling paired with silicon measurements validates chip-level thermal architecture decisions.
- Reduced thermal gradients across heterogeneous die regions
- Lower throttling risk at peak AI workloads
- Pressure-optimized flow aligned with power maps
- Predictive response validated against silicon measurements
Licensing Model
Coeffici licenses thermal architecture IP as a silicon program layer, aligning microfluidic integration with chip design and package teams.
Partner With Coeffici
Let's define deterministic thermal performance for AI silicon.
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