Write once. Optimize forever.
Deploy kernels that autonomously improve performance without retraining. No external optimizers. No training data. Just measurement-driven evolution.
Phase 10 production metrics: Stages 1-2 complete, 0 unplanned rollbacks
Traditional GPU optimization is a 3-month cycle: profile kernel → model behavior → retrain → deploy. Goblin eliminates this cycle entirely.
Declare your computation intent once. MOSMIL compiles to Metal, CUDA, Q9, or CPU automatically.
Push to production. No external optimizer needed. No training data required.
Kernel autonomously mutates parameters, measures fitness in real execution, locks in improvements.
Each mutation targets 12-24% throughput gain. 36.8% succeed. Zero human tuning required.
| Dimension | TVM / Ansor | Goblin |
|---|---|---|
| Optimization Cycle | Offline (3-7 days) | Online (continuous) |
| External Dependencies | Yes (autotuning infra) | None (self-contained) |
| Learning Feedback | Static (offline) | Dynamic (live execution) |
| Multi-Substrate | Manual per target | Automatic (1 MOSMIL spec) |
| Verifiability | Black-box neural predictor | Closed-loop + Q9 cost model |
Three ways to get started. Pick what fits your team.
Kernel-as-a-Service for teams
Strategic partnership & integration
Full technology transfer
Eliminates the kernel optimization bottleneck. Instead of 70% of ML effort spent tuning, Goblin makes tuning autonomous. Phase 10 proves 10K-venture production viability.
MOSMIL DSL is familiar: declare computation intent, specify constraints, let framework handle substrate mapping. FORGE.EVOLVE runs during normal execution.
Closed-loop autonomous optimization at compute layer. Genetic algorithm (mutation) + fitness evaluation (execution) + convergence detection (lock-after-N).