Redefining compute physics.
"We hypothesize that heterogeneous compute orchestration and technological breakthroughs in compute physics are the keys to unlocking AGI."
We are moving the industry from Empirical Compute (trial-and-error) to Predictive Compute (learned physics). By training foundational AI models to learn the laws of silicon, we decouple intelligence from infrastructure.
Foundational Models
HARDWARE PROFILER
HP1
SystemsIngests vendor documentation and hardware specs to generate detailed performance models for each chip architecture. It understands memory bandwidth, compute capabilities, power characteristics, and thermal limits to discover optimal operation mappings per hardware type.
GRAPH PARTITIONER
GP1
CompilersAnalyzes neural network computation graphs to intelligently split workloads across heterogeneous hardware. It minimizes data movement and communication overhead by learning optimal partition strategies from execution data.
CODE GENERATOR
CG1
CompilersGenerates hardware-specific optimized implementations, applying vendor-specific optimizations (CUDA for NVIDIA, ROCm for AMD, etc.). It produces efficient kernels for each target architecture and continuously improves generated code quality.
PERFORMANCE OPTIMIZER
PO1
SystemsMonitors real-time execution metrics to identify bottlenecks across the heterogeneous system. It learns from performance patterns and continuously improves routing decisions based on live feedback.
RESOURCE SCHEDULER
RS1
InfrastructureRoutes workloads to optimal hardware in real-time, balancing cost vs. latency vs. availability. It handles dynamic resource allocation by learning demand patterns and optimizing proactively.
