Edge AI Deployment
Architected and deployed hardware-optimized AI pipelines across 200+ NVIDIA Jetson devices, achieving 40+ FPS real-time inference for 15+ commercial products.
40+
FPS
200+
Devices Deployed
15+
Products Shipped
8x
Speedup
The Challenge
The company had powerful AI models that worked great in the cloud but were too slow and expensive to deploy at scale. Customers needed real-time inference at the edge—in retail stores, warehouses, and public spaces—without cloud connectivity dependencies. The challenge was making sophisticated AI run on constrained edge hardware while maintaining accuracy.
Key Challenges
- •Models optimized for cloud GPUs were 10x too slow for edge devices
- •Memory constraints on Jetson devices (4-8GB) vs training machines (32GB+)
- •Need for consistent performance across varying environmental conditions
- •Deploying and managing updates across 200+ distributed devices
- •Power consumption limits in some deployment scenarios
The Solution
I developed a comprehensive edge optimization pipeline that transforms cloud-trained models into edge-ready deployments. The pipeline includes automated quantization, architecture optimization, and TensorRT compilation. I also built a device management platform for OTA updates and monitoring.
Built automated INT8 quantization pipeline with calibration dataset curation
Implemented model architecture optimization including layer fusion and channel pruning
Developed TensorRT-native inference engine with custom plugins for unsupported operations
Created dynamic batching system that balances latency and throughput based on load
Built Docker-based deployment system with OTA update capabilities
System Architecture
The architecture separates model optimization (done once in cloud) from deployment (repeated across devices), enabling rapid iteration while maintaining consistency.
Model Optimization Pipeline
Cloud-based pipeline that takes a trained model and produces optimized artifacts. Includes ONNX export, quantization, TensorRT compilation, and validation.
Edge Inference Engine
C++ inference runtime built on TensorRT with custom memory management. Handles multi-model orchestration and resource sharing.
Preprocessing Accelerator
CUDA-accelerated preprocessing (resize, normalize, color conversion) eliminating CPU bottlenecks. Uses unified memory for zero-copy transfers.
Device Management Platform
Central platform for device health monitoring, model deployment, and configuration management. Supports staged rollouts and automatic rollback.
Telemetry System
Lightweight telemetry collecting inference metrics, hardware stats, and anomaly flags. Enables proactive maintenance and optimization.
Key Implementation Details
INT8 Quantization Without Accuracy Loss
Naive INT8 quantization often degrades accuracy. I developed a calibration dataset curation process that selects representative samples covering the full data distribution. Combined with per-tensor quantization for sensitive layers, we maintained 99%+ of original accuracy while achieving 3-4x speedup.
Custom TensorRT Plugins
Some model operations weren't natively supported by TensorRT. I wrote custom CUDA plugins for operations like deformable convolutions and custom attention mechanisms, enabling end-to-end TensorRT execution without CPU fallbacks.
Memory-Efficient Multi-Model Serving
Many products require multiple models (detection + classification + tracking). I implemented a shared memory pool across models and dynamic model loading/unloading based on usage patterns. This enabled running 3-4 models on devices that couldn't fit them all simultaneously.
Thermal-Aware Inference
Sustained high load causes thermal throttling on edge devices. I built a thermal management system that monitors device temperature and adjusts inference frequency and batch size to maintain consistent performance while preventing overheating.
Tech Stack
Optimization
Edge Devices
Development
Infrastructure
Key Learnings
Edge optimization should be considered from model design, not as an afterthought
Quantization-aware training produces much better results than post-training quantization
Device management at scale requires treating deployments like software releases—staged rollouts, rollback capability
Thermal and power management are as important as raw performance for edge devices
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