From model training to silicon deployment — we bring AI inference to embedded devices. TensorFlow Lite, ONNX, OpenVINO, and Vitis AI on automotive, industrial, and consumer SoC platforms.
Edge AI eliminates cloud dependency for latency-critical, privacy-sensitive, and bandwidth-constrained applications. Promwad deploys trained models directly on embedded SoCs, FPGAs, and microcontrollers — achieving real-time inference at milliwatt power budgets where cloud-based solutions cannot operate.
Our edge AI practice spans the full pipeline: dataset curation and augmentation, model architecture selection (YOLO, EfficientNet, MobileNet, custom architectures), quantization and pruning for target hardware, runtime optimization with vendor-specific tools, and continuous model update via OTA. We work across automotive (ADAS, DMS), industrial (anomaly detection, quality inspection), and consumer (gesture recognition, voice processing) domains.
Optimized a multi-sensor perception stack (camera + LiDAR + radar fusion) on NVIDIA Orin for a European autonomous driving company. Redesigned the inference pipeline to reduce end-to-end latency while improving detection accuracy across pedestrians, cyclists, and vehicles in adverse weather conditions.
Deployed a YOLOv8-based defect detection system on Ambarella CV25 for a PCB assembly line. The system inspects solder joints, component placement, and surface defects at 120 units/minute with sub-millimeter precision. Runs entirely on-device with no cloud connectivity.
Developed a TinyML anomaly detection system on NXP i.MX RT1170 for a European compressor manufacturer. MEMS accelerometer data is processed on-device using a lightweight autoencoder model. Anomalies trigger alerts via MQTT to the fleet management platform.
Client identities changed. Methodologies and outcomes are real.
Camera-to-decision pipeline for object detection, classification, and tracking. Runs entirely on-device with INT8 quantized models.
Multi-sensor anomaly detection on microcontroller-class hardware. TinyML autoencoder model with MQTT alerting and optional cloud analytics.
Edge AI runs inference on the device itself — no network dependency, sub-millisecond latency, full data privacy. Cloud AI offers unlimited compute but requires connectivity, adds 50-200ms latency, and raises data sovereignty concerns. Most industrial and automotive applications need edge-first architectures with optional cloud sync for model updates and fleet analytics.
It depends on the model complexity and your existing processor. We routinely deploy lightweight models (anomaly detection, keyword spotting) on Cortex-M class MCUs. Object detection typically requires a dedicated NPU or GPU — Ambarella CV25, NXP i.MX 8M Plus, or NVIDIA Jetson Nano are common cost-effective options. We evaluate your hardware during the feasibility phase.
We integrate AI model updates into the device OTA pipeline (SWUpdate or RAUC). Models are versioned, signed, and delivered with A/B partition support for safe rollback. For automotive applications, this aligns with UNECE R156 software update management requirements.
Properly quantized INT8 models typically achieve within 1-3% of their FP32 cloud counterparts. For most industrial and automotive applications, this gap is negligible. We validate accuracy against your specific dataset and acceptance criteria before deployment.