- WAVE 5 // DEVELOPMENT
- HARDWARE CO-DESIGN
- ROOFLINE MODEL
FORGE
EDGE VLA SCALING LAWS
Hardware co-design scaling laws for VLA edge deployment. Roofline modeling predicts latency, throughput, and memory usage on constrained hardware — Jetson, Orin, mobile GPUs. Pareto frontier search for optimal model size vs accuracy trade-offs with hardware-certified deployment claims.
MODULE STATUS: DEVELOPMENTATOMOS
PARETO
- DIVISION
- ANIMA
- WAVE
- W5
- DOMAIN
- HARDWARE & SENSORS
- WAVE 5 // ANIMA SUITE
- HARDWARE — EDGE VLA SCALING LAWS
VLA MODELS DON'T FIT ON REAL HARDWARE
Vision-Language-Action models are designed for cloud GPUs — billions of parameters, gigabytes of memory, hundreds of watts. But robots operate on edge hardware: Jetson Orin with 32GB, mobile GPUs with strict thermal budgets. Nobody knows which model configuration actually runs at the required FPS on the target hardware.
Current deployment is trial-and-error: train a model, try to deploy, discover it's too slow, shrink it, lose accuracy, repeat. There are no analytical tools that predict deployment performance before training. FORGE provides the scaling laws and roofline analysis to certify deployment claims before a single training run.
WHAT FORGE DELIVERS
FORGE builds analytical roofline models for VLA architectures on specific hardware targets, enabling Pareto frontier search across model size, accuracy, latency, and memory. Every deployment claim is hardware-certified through both analytical prediction and empirical validation.
PIPELINE
- 01Hardware profiling — characterize compute, memory bandwidth, and thermal limits of target edge devices
- 02Roofline modeling — map VLA architecture operations to hardware capability ceilings for latency/throughput prediction
- 03Pareto frontier search — sweep model configurations to find optimal accuracy vs hardware cost trade-offs
- 04Deployment certification — validate analytical predictions with empirical benchmarks, issue hardware-certified claims
CAPABILITIES
- ROOFLINE ANALYSISAnalytical performance prediction for any VLA config on any target hardware→ Know deployment feasibility before training — no more trial and error
- PARETO FRONTIERAutomated search for optimal model size vs accuracy on constrained hardware→ Find the best model that actually fits your hardware budget
- CERTIFIED CLAIMSHardware-validated deployment performance guarantees with confidence intervals→ Provable FPS and latency claims — not marketing, measurement
- HARDWARE CO-DESIGNJoint optimization of model architecture and hardware deployment configuration→ Model and hardware decisions informed by the same analytical framework
WHY THIS IS HARD
Bridging the gap between theoretical FLOPs and real hardware performance:
- 01Roofline models must account for memory hierarchy effects — L1/L2 cache, DRAM bandwidth, NVMe spill — not just peak compute
- 02VLA architectures have irregular compute graphs — attention, convolution, tokenization — each with different hardware bottlenecks
- 03Thermal throttling on edge devices means sustained performance differs from burst performance by 30-50%
- 04Quantization effects on accuracy are model-specific and hardware-specific — INT8 on Orin differs from INT8 on mobile GPU
- 05Pareto search space is combinatorial: model width × depth × attention heads × quantization × batch size × hardware config
FORGE solves this through ATOMOS temporal profiling combined with hardware-specific roofline models. Analytical predictions are validated empirically on each target device, building a database of certified deployment configurations.
SYSTEM PERFORMANCE
Measured across hardware co-design pipeline:
| METRIC | VALUE |
|---|---|
| Deployment Certification | Hardware-certified claims with empirical validation |
| Pareto Search | Automated frontier discovery across config space |
| Prediction Method | Analytical roofline + empirical benchmarks |
| Co-Design Scope | Model architecture ↔ hardware deployment joint optimization |
WHAT'S BUILT TODAY
| COMPONENT | STATUS | NOTES |
|---|---|---|
| Roofline Analysis | COMPLETE | Analytical models for Jetson Orin, mobile GPU targets |
| Profiling Framework | COMPLETE | Automated hardware characterization pipeline |
| Pareto Frontier Search | IN PROGRESS | Configuration sweep with accuracy/latency trade-offs |
| Deployment Certificates | IN PROGRESS | Certified claim generation with confidence intervals |
| Core models | COMPLETE | ATOMOS temporal profiling production-ready |
| API layer | IN PROGRESS | Public API, pending infrastructure |
WHERE FORGE DEPLOYS
- APP_01
EDGE ROBOT DEPLOYMENT
Certify that a VLA model runs at required FPS on Jetson Orin before training — know the hardware budget constraints upfront and design models to fit.
- APP_02
MODEL ARCHITECTURE SEARCH
Navigate the Pareto frontier of accuracy vs latency for a specific hardware target — find the sweet spot where model capability meets deployment reality.
- APP_03
FLEET HARDWARE PLANNING
Determine which edge hardware to purchase for a given VLA workload — roofline analysis predicts whether Orin, Nano, or mobile GPU meets requirements.
UNDER THE HOOD
FOUNDATION: ROOFLINE METHODOLOGY
- Hardware characterization: compute peak, memory bandwidth, cache hierarchy, thermal envelope
- Operation mapping: VLA compute graph → hardware resource utilization model
- Bottleneck analysis: compute-bound vs memory-bound classification per layer
- Scaling laws: how accuracy and latency change with model size on specific hardware
FORGE IMPLEMENTATION
- Roofline builder: automated hardware profiling with micro-benchmarks
- VLA analyzer: operation-level compute graph extraction and resource mapping
- Pareto engine: multi-objective optimization across model and hardware configurations
- Certification pipeline: analytical prediction → empirical validation → confidence scoring
ANIMA MODULE INTEGRATION
- ATOMOS provides temporal profiling and sequence-level performance modeling
- Roofline predictions validated against ATOMOS real-time execution traces
- Combined as hardware-aware deployment certification for VLA models
TARGET PLATFORMS
- NVIDIA Jetson Orin (64GB/32GB)
- NVIDIA Jetson Nano (4GB/8GB)
- Mobile GPUs (Adreno, Mali)
- Custom edge accelerators
RESEARCH BASIS
- [01]Hardware co-design scaling laws for VLA edge deployment — roofline analysis meets Pareto frontier search for certified deployment claims