System Architecture
AndonAI multi-agent pipeline architecture and AMD GPU acceleration design
Agent Pipeline Architecture
Input Layer
Multimodal evidence ingestion
Watcher Agent
Real-time anomaly detection
Interpreter Agent
Contextual analysis
SOP Agent
Procedure matching
Root Cause Agent
Causal analysis
Dispatcher Agent
Severity & assignment
Report Agent
Audit-ready documentation
Dashboard
Command board visualization
AMD GPU Acceleration
Designed for AMD Developer Cloud & AMD Instinct MI300X
AMD Instinct MI300X
192GB HBM3 memory, up to 1,307 TFLOPS FP16 performance for multimodal AI inference at scale.
ROCm Open Platform
Open-source GPU computing platform enabling portable AI workloads with PyTorch and ONNX Runtime.
AMD Developer Cloud
Cloud-based access to MI300X accelerators for development, testing, and production deployment.
Batch Processing
Parallel video frame analysis, embedding generation, and multi-agent workload distribution.
Performance Benchmark
CPU vs AMD Instinct MI300X GPU comparison
Demo benchmark placeholder — Replace with real values via environment variables or the AMD inference service.
Single Frame Analysis
2340ms → 180ms
Multimodal Inference
4500ms → 320ms
Batch Video Processing (60 frames)
12000ms → 850ms
Embedding Generation (1K docs)
1800ms → 95ms
Hardware: AMD Instinct MI300X (Simulated) • Simulated benchmark values
AMD GPU Workload Distribution
Batch Video Frame Analysis
Process 60+ camera frames in parallel using ROCm-optimized ONNX Runtime on MI300X 192GB HBM3 memory.
Multimodal Inference
Run vision-language models for scene understanding, object detection, and hazard classification at scale.
Embedding Generation
Generate text and image embeddings for SOP matching and similarity search across the knowledge base.
Agent Workload Parallelization
Distribute independent agent tasks across GPU compute units for sub-second analysis pipeline completion.
Deployment Architecture
AMD Developer Cloud
Cloud-based MI300X instances for development, benchmarking, and production AI inference workloads.
ROCm Stack
Open-source GPU platform with PyTorch, ONNX Runtime, and vLLM for serving multimodal models.
Inference Service
FastAPI-based Python service with /analyze-frame, /batch-analyze, and /benchmark endpoints for GPU inference.
Frontend Application
Next.js 14+ dashboard consuming inference results via REST API. Deployable on any cloud or edge infrastructure.
For Judges: Benchmark values shown are simulated placeholders. Real benchmarks can be supplied via environment variables (BENCHMARK_FRAME_CPU, BENCHMARK_FRAME_GPU, etc.) or by connecting the AMD inference service running on AMD Developer Cloud with actual MI300X hardware.