Edge Compute
Compute Platform Selection
The compute platform strategy will balance development flexibility with production performance requirements, using different hardware configurations optimized for each project phase. This approach will enable rapid prototyping while ensuring production systems meet stringent performance and reliability standards.
The compute platform strategy will balance performance requirements with deployment practicality across different project phases. Starting with development-friendly hardware for initial validation, the system will transition to production-optimized platforms that can handle the demanding requirements of live competitions.
Development Platform (Alpha Phase)
The alpha testing phase will use NVIDIA Jetson Orin NX modules as the baseline compute platform. These edge AI devices (specialized computers for artificial intelligence at the location) will provide an optimal balance of performance, power efficiency, and cost for initial algorithm validation. Each Orin NX will deliver up to 100 TOPS (trillion operations per second) of AI compute performance, sufficient for processing 4-8 camera streams with TensorRT-optimized models (software that speeds up AI calculations). The unified memory architecture (shared memory between CPU and GPU) will simplify data movement between CPU and GPU, reducing latency in the inference pipeline (AI processing chain).
Production Platform (Beta/Release)
For large-scale championship events and venues requiring maximum reliability, production deployment can utilize more powerful edge servers using NVIDIA RTX 4060 series GPUs or Tesla T4 accelerators (specialized graphics cards for AI processing). These platforms will support the NVIDIA DeepStream SDK (software development kit for video processing), enabling efficient multi-stream processing with hardware-accelerated video decode/encode. Each production server will handle 16-32 wall ball stations, providing redundancy through N+1 configuration (backup systems). The increased compute capacity will accommodate additional processing overhead from production features like audit logging and real-time analytics.
Platform Configuration Comparison
| Configuration | Compute Unit | Stations/Unit | Latency | Cost/Kit (8 stations) | Use Case |
|---|---|---|---|---|---|
| Development | Jetson Orin NX | 4-8 | <200ms | $20,400 | Alpha testing, regional events |
| Production | RTX 4060 Server | 16-32 | <150ms | $28,000 | Championships, world finals |
The dual-track hardware strategy allows HYROX to optimize deployment costs based on event requirements. The Jetson-based configuration provides excellent performance for most events at lower cost, while the edge server configuration offers enhanced reliability and capacity for critical championship events.
Performance Optimization
The optimization framework will maximize edge hardware performance through comprehensive model and system-level enhancements.
Model Optimization Pipeline will apply aggressive optimization techniques for edge deployment without sacrificing judging accuracy. Neural network models will undergo quantization to INT8 precision (reducing data size for faster processing), layer fusion to reduce memory bandwidth, and pruning of redundant connections (removing unnecessary parts).
TensorRT Integration will automatically optimize models for specific GPU architectures, achieving 3-5x speedup over baseline implementations while maintaining inference quality.
Custom CUDA Kernels (specialized GPU code) will handle specialized operations like multi-view triangulation that benefit from hardware-specific optimization, providing performance gains unavailable through standard frameworks.
Memory Management will enable efficient processing of multiple video streams on limited edge hardware through zero-copy pipelines (direct data access without copying) that eliminate redundant data transfers.
Ring Buffer Architectures (circular data storage) will maintain recent frame history for temporal models while bounding memory usage, and dynamic batch sizing will adjust to current load for optimal resource use.
Distributed Architecture
The distributed computing architecture will ensure scalability and reliability through intelligent load distribution and redundancy mechanisms. This design will enable horizontal scaling (adding more computers) to support large-scale events while maintaining consistent performance and failover capabilities (backup systems).
Load Distribution Strategy
The system will use intelligent load distribution across available compute nodes (individual computers) based on real-time performance metrics. Each node will report current use, processing latency (delay), and available capacity to a central coordinator. New camera streams will be assigned to nodes with optimal capacity, considering both current load and network topology (network layout). Dynamic rebalancing will migrate streams between nodes if performance degrades, maintaining consistent latency across all stations.
Failover Mechanisms
High availability architecture (system designed to keep running) will ensure continued operation despite hardware failures. Each compute node will maintain heartbeat communication (regular status signals) with redundant coordinators. Upon node failure detection (missing heartbeats for 3 seconds), affected camera streams will automatically migrate to backup nodes. State synchronization (keeping backup systems current) between primary and backup nodes will minimize disruption, typically completing failover within 5 seconds.
Thermal Management
Effective thermal management will ensure consistent performance and hardware longevity in demanding competition environments. Sophisticated cooling solutions will prevent thermal throttling (performance reduction due to heat) while maintaining quiet operation suitable for sports venues.
Cooling Requirements
Edge compute hardware will generate significant heat under continuous load, requiring active thermal management. Compute enclosures will include high-CFM fans (high air flow) with dust filters suitable for venue environments. Heat sinks with vapor chambers will efficiently dissipate GPU heat, maintaining junction temperatures below 75°C. Thermal throttling profiles (heat management settings) will prioritize consistent performance over peak throughput, preventing sudden performance drops during competitions.
Environmental Considerations
Venue conditions will vary from climate-controlled arenas to outdoor tents with ambient temperatures exceeding 40°C. Compute hardware specifications will include industrial temperature ratings (-20°C to 60°C ambient). Thermal monitoring will trigger protective measures including load shedding (reducing workload) and emergency shutdown if temperatures exceed safe operating ranges. Redundant systems will allow graceful degradation rather than complete failure under extreme conditions.
Software Stack
The software stack will provide a stable, optimized foundation for computer vision and machine learning workloads while maintaining security and maintainability standards essential for production deployment.
Operating System and Drivers
Ubuntu 22.04 LTS (Long Term Support Linux operating system) will provide a stable, well-supported foundation for edge deployments. Real-time kernel patches (system modifications) will reduce scheduling latency for time-critical operations. NVIDIA driver version locking will prevent unexpected behavior changes from automatic updates. Container-based deployment via Docker (software packaging system) will ensure consistent deployment across heterogeneous hardware (different computer types), with orchestration tools managing multi-node clusters in large deployments as needed.
Inference Framework
TensorRT will serve as the primary inference engine (AI processing system), providing optimal performance on NVIDIA hardware. The framework will support all required model architectures including YOLO for detection (object identification), RTMPose for 2D pose estimation (body position detection), and temporal CNNs (time-aware neural networks) for 3D lifting (converting 2D to 3D positions). ONNX Runtime will provide a fallback option for models not yet optimized for TensorRT. The inference pipeline will maintain model versioning to enable A/B testing and gradual rollouts of improvements.
Monitoring and Diagnostics
Comprehensive monitoring and diagnostic capabilities will ensure reliable operation and proactive maintenance of edge computing infrastructure. Real-time telemetry (system monitoring data) will enable rapid issue identification and resolution during critical competition periods.
Performance Telemetry
Comprehensive telemetry will capture system performance metrics at multiple granularities (detail levels). GPU use, memory bandwidth, and inference latency will be sampled at 1Hz for real-time monitoring. Prometheus exporters (monitoring tools) will expose metrics for centralized collection and visualization through Grafana dashboards (monitoring displays). Alert rules will trigger notifications when metrics exceed thresholds, enabling proactive intervention before athlete experience degrades.
Diagnostic Capabilities
Built-in diagnostic modes will facilitate troubleshooting without disrupting ongoing competitions. Frame capture modes will record problematic sequences for offline analysis. Inference visualization will overlay pose skeletons (body position outlines) and confidence scores (accuracy ratings) on video streams. Performance profiling will identify bottlenecks (slowdowns) in the processing pipeline. Remote access capabilities will allow expert support without on-site presence, critical for global event support.