Edge AI & Advanced Sensing

Edge AI is the deployment of machine learning inference directly on embedded devices — microcontrollers, FPGAs, and dedicated neural processing units (NPUs) — without sending data to the cloud. This enables real-time decision-making with sub-10ms latency, complete data privacy (no data leaves the device), and operation in disconnected or bandwidth-constrained environments.

Inovasense designs custom Edge AI hardware optimized for on-device inference, supporting frameworks including TensorFlow Lite for Microcontrollers (TFLM), ONNX Runtime, Edge Impulse, and ExecuTorch, deployed on ARM Cortex-M, RISC-V, and FPGA-based platforms.

Why Edge AI Over Cloud AI in 2026?

The convergence of three forces makes Edge AI the dominant architecture in 2026: the EU AI Act requiring risk classification and documentation of AI systems, data sovereignty regulations (GDPR, NIS2) restricting data transfers, and NPU hardware maturation making on-device inference cost-competitive with cloud APIs.

Factor	Edge AI	Cloud AI
Latency	<10 ms (on-device)	100–500 ms (network dependent)
Privacy	Data never leaves device	Data transmitted to third-party servers
Bandwidth cost	Zero (local processing)	Significant (continuous streaming)
Reliability	Works offline	Requires internet connection
Power	µW–mW range (TinyML)	Watts (GPU servers)
Per-inference cost	Zero (hardware is fixed cost)	Per-API-call billing
AI Act compliance	Simplified (local, auditable)	Complex (third-party processing)
Model updates	OTA to device	API version management

When to choose Edge AI: Industrial anomaly detection, predictive maintenance, visual inspection, autonomous navigation, environmental monitoring, wearable health, counter-UAS, and any application where latency, privacy, EU AI Act compliance, or connectivity constraints make cloud processing impractical.

Our Edge AI Hardware Platform Expertise (2026)

Microcontroller-Based AI (TinyML)

For ultra-low power applications requiring continuous sensing:

Platform	AI Accelerator	Performance	Typical Workload	Power
STM32N6 (Cortex-M55 + Helium)	Neural-Art NPU	600 GOPS	Image classification, keyword spotting	10–50 mW
Arm Cortex-M85 + Ethos-U85	Ethos-U85 NPU	1 TOPS	Multi-model inference, on-device fine-tuning	30–100 mW
Alif Ensemble E7	Ethos-U55 + DSP	500 GOPS	Vision + audio fusion	20–60 mW
NXP MCX N94x (Cortex-M33)	eIQ Neutron NPU	150 GOPS	Gesture recognition, predictive maintenance	15–40 mW
Espressif ESP32-P4	RISC-V + AI extensions	400 GOPS	On-device LLM (small), sensor fusion	30–100 mW
Nordic nRF54H20	RISC-V + Cortex-M33	Multiple cores	BLE + AI wearables	5–15 mW
Infineon PSoC Edge	Arm Ethos-U55	256 GOPS	Predictive maintenance, anomaly detection	10–30 mW

FPGA-Based AI Acceleration

For applications requiring higher throughput or custom datapath architectures:

• AMD Vitis AI (2024+) — DPU IP for CNN/transformer inference on Versal AI Edge (up to 400 TOPS INT8)
• Lattice sensAI 2.0 — Ultra-low power ML inference on Avant-G and CrossLink-NX (<1 mW always-on)
• Custom architectures — Application-specific accelerators for unique model topologies: spiking neural networks (SNN), state space models (Mamba), and quantized transformer attention blocks

Application Processor AI

For vision-heavy and multi-model workloads:

• NVIDIA Jetson Orin NX/Nano — Up to 100 TOPS GPU-accelerated inference for multi-camera vision systems
• NXP i.MX 95 — Dedicated NPU (2 TOPS) + GPU for industrial vision and voice, with functional safety
• Texas Instruments AM62A — Vision processor with dedicated deep learning accelerator for factory automation
• Rockchip RK3588 — 6 TOPS NPU for edge gateways with multi-stream video analytics

On-Device AI Trends (2026)

Small Language Models (SLMs) on Edge

The emergence of sub-1B parameter language models (Phi-3 mini, Gemma 2B, SmolLM) enables conversational AI on edge devices:

• Audio-to-text — Whisper Tiny/Base running on Cortex-M85 with Ethos-U85 NPU
• Text generation — 0.5–2B parameter models on application processors with 1–4 GB RAM
• RAG on edge — Retrieval-augmented generation using local vector stores for domain-specific knowledge

Multimodal Fusion

Combining vision, audio, IMU, and environmental data in a single inference pipeline for richer context — e.g., combining acoustic anomaly detection with vibration and thermal data for industrial predictive maintenance.

Sensing & Sensor Fusion

We design complete sensing systems that combine multiple sensor modalities for higher accuracy and contextual awareness:

Sensor Integration

• Environmental — Temperature (±0.1°C accuracy), humidity, pressure, air quality (VOC, PM2.5/PM10)
• Motion — 9-axis IMU (accelerometer + gyroscope + magnetometer), high-g shock sensors
• Optical — Time-of-Flight (ToF) distance, ambient light, spectral analysis (NIR, SWIR), multispectral imaging
• Acoustic — MEMS microphone arrays for sound classification, beamforming, acoustic event detection
• Radar — mmWave (60/77/79 GHz) for presence detection, vital signs monitoring, gesture recognition
• Custom sensors — Application-specific transducers designed to specification

Sensor Fusion Algorithms

We implement Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), particle filters, and neural network-based fusion running directly on the sensor MCU — eliminating the need for external processing and achieving <1 ms fusion latency.

Computer Vision at the Edge

• Object detection — YOLOv8-nano/YOLOv10 and MobileNet-SSD optimized for INT8 inference on NPU hardware
• Image classification — EfficientNet-Lite, MobileNetV4 with <5 ms inference on Cortex-M85
• Semantic segmentation — DeepLabV3+, PP-LiteSeg for scene understanding in autonomous systems
• Optical flow — Real-time motion estimation for stabilization, tracking, and visual odometry
• Anomaly detection — Vision Transformer (ViT) autoencoders and PatchCore for manufacturing quality control
• Multi-camera systems — Synchronized multi-stream processing for 360° awareness

Our Development Methodology

1. Data pipeline design — Sensor selection, data collection protocols, annotation strategy (CVAT, Label Studio)
2. Model development — Architecture search (NAS), training on representative data, quantization-aware training (QAT)
3. Optimization — INT8/INT4 quantization, structured pruning, knowledge distillation, ONNX graph optimization
4. Deployment — Model compilation for target hardware (TFLite, ONNX Runtime, TVM, ExecuTorch), runtime integration
5. Validation — On-device accuracy testing, latency profiling, power measurement, thermal characterization
6. Continuous improvement — OTA model updates, edge-cloud feedback loops for model retraining, A/B testing on-device

Compliance & Standards (2026)

Regulation	Effective	Requirement	Our Approach
EU AI Act (2024/1689)	Phased 2025–2027	Risk classification, conformity assessment, documentation	AI risk assessment, technical documentation, human oversight integration
CRA (2024/2847)	2027	Cybersecurity for AI-enabled devices	Secure boot, OTA updates, SBOM for ML models and firmware
IEC 62443	Ongoing	Security for AI-enabled industrial devices	Zone/conduit security model, SL-T assessment
ISO/IEC 42001:2023	Ongoing	AI management systems standard	AIMS implementation for edge AI development
ISO/IEC 22989:2022	Ongoing	AI concepts and terminology	Framework alignment for documentation
GDPR Art. 25	Ongoing	Privacy by design	Local inference (no personal data leaves device), differential privacy
CE marking	Ongoing	EMC and safety	EN 55032/55035, EN 62368-1 for all edge hardware. See Industrial Design for enclosure compliance.

All Edge AI solutions are developed within the European Union, ensuring full data sovereignty and compliance with EU AI governance frameworks. Model training data provenance and bias documentation provided as standard deliverables.