Intelligent Edge
AI/ML Integration & Edge AI
Deploy intelligence directly to resource-constrained hardware for ultra-low latency, reduced cloud bandwidth, and data privacy.
The Challenge
Problem We Solve
Heavy AI models fail to run efficiently on low-power embedded devices without dedicated hardware acceleration and pruning.
Key Deliverables
What You Receive
- Model optimization, quantization (INT8/FP16), and pruning
- Edge TPU/NPU acceleration driver integration
- Computer vision pipelines (object detection, anomaly detection)
- Audio & vibration predictive maintenance ML models
- On-device inferencing SDK and cloud telemetry loop
Engineering Process
How We Deliver
Phase 1: Feasibility & Dataset
1–2 Weeks
Data requirements analysis, baseline benchmarking, and hardware target matching.
Phase 2: Model Compression
2–4 Weeks
Pruning, post-training quantization, and ONNX/TFLite export for MCU/NPU targets.
Phase 3: Hardware Acceleration
3–4 Weeks
HAL driver setup for Hailo, Coral, ST NPU, STM32Cube.AI, or Jetson hardware.
Phase 4: Field Tuning
2–3 Weeks
Real-world accuracy testing, power/thermal benchmarking, and firmware integration.
Technology Stack
Tools & Platforms
TensorFlow Lite Micro
Edge Impulse
ONNX Runtime
OpenVINO
NVIDIA Jetson
Coral TPU
ST Micro Edge AI
OpenCV