Explore design spaces
Rapidly evaluate architecture, topology, and partitioning alternatives.
AI SYSTEM CO-DESIGN & PERFORMANCE INTELLIGENCE
Model AI and hardware together. Optimize both.
Interplane creates an executable representation of AI workloads, compute architectures, memory systems, interconnects, and runtime behavior so teams can identify bottlenecks and evaluate changes before committing to hardware.
Early access for AI infrastructure and semiconductor design teams
Physics-aware modelingHardware + AI co-designRuntime policy exploration
AN EXECUTABLE MODEL OF YOUR AI SYSTEM
AI Workload / Model
- Model architecture
- Precision
- Batching
- Partitioning
- Communication patterns
- Runtime and scheduling
InterplaneModel · Analyze · Optimize
Map & Execute
Insights & Recommendations
Hardware System
- Compute
- Memory and storage
- Chiplets
- Topology
- Interconnect and I/O
- Power and thermal constraints
Outcomes
Make better decisions across the AI hardware lifecycle.
Uncover bottlenecks
Identify where compute, memory, interconnect, and I/O limit performance.
Co-optimize AI and hardware
Tune models and hardware together for greater efficiency.
Reduce risk and cost
Avoid expensive respins and infrastructure mistakes before committing.
Inform runtime policies
Design better scheduling, placement, routing, and load-balancing policies.
Scale with confidence
Analyze systems from chip and package through rack and data center.
How It Works
Start broad, then add detail where it changes a decision.
Interplane supports an executable world model for AI-system performance: a structured representation of workload, hardware, interconnect, and runtime choices that can be explored together.
Architecture Insight
Compare architecture choices quickly and identify where performance is likely to be constrained.
Performance Insight
Understand how complete workloads interact with compute, memory, and communication resources.
Interconnect Insight
Examine communication behavior across complex, connected systems.
Current results are internally modeled and uncalibrated. They support controlled relative comparisons and bottleneck analysis, not yet hardware-calibrated wall-clock prediction.
Use Cases
Built for architecture decisions where models and systems meet.
AI accelerator architecture
Compare accelerator, memory, fabric, and package choices around real workload behavior.
Chiplet and heterogeneous integration
Study how chiplets, interposers, I/O, and packaging constraints shape the full system.
Memory and interconnect planning
Evaluate where data movement, memory pressure, and topology choices create bottlenecks.
Runtime and workload placement
Explore scheduling, placement, routing, and load-balancing policies before deployment.
AI infrastructure co-design
Connect chip, package, board, rack, and data-center questions in one design loop.
Early Access
Design with the workload and hardware in the same loop.
Interplane is working with design partners on architecture studies, performance modeling, and hardware-software co-design.