Edge AI Solutions for Indian and Global Businesses

TinyML is machine learning inference on microcontrollers - the smallest, lowest-power class of processors (Arm Cortex-M series, RISC-V) with as little as 256KB of RAM and running on microwatts to milliwatts of power. TinyML enables AI on hardware like STM32 microcontrollers, Arduino Nano 33 BLE Sense, ESP32-S3, and Nordic nRF52840 - the same chips used in industrial sensors, wearable devices, and IoT nodes. TinyML models are created by training a neural network on a more powerful computer, then compressing it through quantisation (converting FP32 weights to INT8), pruning, and knowledge distillation to fit within kilobytes while maintaining acceptable accuracy. The TensorFlow Lite Micro framework and Edge Impulse platform are the primary TinyML deployment tools.

Model quantisation is the process of reducing the numerical precision of neural network weights and activations - from 32-bit floating-point (FP32, 4 bytes per weight) to 8-bit integer (INT8, 1 byte per weight) or even 4-bit integer. Quantisation reduces model size by 4-8x and inference speed by 2-4x, while typically losing only 1-3% accuracy if done correctly. It is essential for edge AI because edge hardware has limited memory (a Cortex-M7 microcontroller has 1MB RAM vs a cloud server's gigabytes) and limited compute (no floating-point acceleration on low-end MCUs). Post-training quantisation (PTQ) quantises a trained FP32 model using a calibration dataset. Quantisation-aware training (QAT) simulates quantisation during training for better accuracy at the cost of longer training time.

NVIDIA Jetson is a family of embedded AI computing modules designed for edge AI applications requiring significant compute: Jetson Nano (0.5 TOPS GPU, entry-level vision), Jetson Orin NX (40/100 TOPS, mid-range industrial AI), and Jetson AGX Orin (275 TOPS, demanding robotics and autonomous systems). Jetson modules run Linux (JetPack SDK), support NVIDIA's full software stack (CUDA, cuDNN, TensorRT, DeepStream), and are designed for industrial-grade deployment - wide temperature range, long production lifetime, and certified for industrial environments. Jetson is used for production line vision systems processing multiple camera streams in real time, autonomous mobile robots, smart city infrastructure (traffic AI, crowd monitoring), and any application requiring GPU-accelerated inference without cloud dependency.

Edge AI model updates are managed via OTA (Over-The-Air) update systems. The process: (1) A new model version is trained and validated off-device; (2) The model is packaged with a version number and cryptographic signature; (3) The OTA server pushes the package to eligible devices (all devices, or a staged rollout starting with a test group); (4) The device downloads the package, verifies the signature, and installs the new model; (5) The device reports its new model version and initial accuracy metrics back to the fleet management dashboard; (6) If accuracy metrics fall below threshold, automatic rollback to the previous version occurs. OTA systems for edge AI must handle: offline devices (queuing updates for when connectivity is restored), low-bandwidth connections (differential updates), interrupted downloads (resume capability), and atomic updates (device must not be in a broken state if power is lost mid-update).

Federated learning is a distributed machine learning approach where each edge device trains the AI model on its own local data - the data never leaves the device. Instead of transmitting raw data to a central server, each device transmits only the model weight updates (gradients) learned from its local data. A central server aggregates these gradients (using FedAvg or similar algorithm) to update a global model, then distributes the improved global model back to all devices. This allows models to continuously improve from real production data without ever centralising that data. Federated learning is particularly valuable for privacy-sensitive edge AI applications: medical devices where patient data must not leave the clinical environment; financial devices where transaction data is confidential; and industrial equipment where process parameters are proprietary.

Yes - this is one of edge AI's primary advantages over cloud AI. Once an edge AI model is deployed on a device, it runs entirely on-device without any network calls for inference. A production line defect detector on a Jetson Nano continues to inspect parts and reject defects even when the factory's internet connection is down. A wearable health monitor continues to detect anomalies even in areas with no mobile signal. The only network dependency for edge AI is model updates - and these can be batched and applied when connectivity is available, or delivered via local network (the devices synchronise with an on-premises update server that itself syncs to the cloud when connectivity allows). For TinyML on microcontrollers, models are compiled into the firmware and require no network connectivity even for updates (firmware is updated via JTAG or local serial during scheduled maintenance).

Edge AI models typically achieve 90-97% of equivalent cloud model accuracy after optimisation, depending on the compression ratio required. Quantisation from FP32 to INT8 typically causes 1-3% accuracy drop with proper calibration. More aggressive compression (INT4, heavy pruning) causes larger drops. For well-defined edge AI tasks (binary defect classification, activity recognition, keyword detection), accuracy above 92-95% is routinely achievable on constrained hardware. For complex tasks (fine-grained object recognition across hundreds of classes, complex semantic segmentation), the gap between edge and cloud models is larger - in these cases, hybrid architectures are used: the edge device handles most cases, and uncertain cases (low confidence score) are escalated to cloud processing. The accuracy-latency-power tradeoff is specific to each application - we quantify it empirically on target hardware before committing to an architecture.

TinyML and microcontroller AI, embedded computer vision, NVIDIA Jetson AI deployment, edge AI model optimisation, industrial IoT AI edge systems, smart camera and vision systems, federated learning, and edge AI fleet management.

Yes. Evolution Infosystem develops TinyML systems on STM32, Arduino Nano 33 BLE, ESP32-S3, and Nordic nRF52840 microcontrollers using TensorFlow Lite Micro, Edge Impulse, and CMSIS-NN - for vibration anomaly detection, keyword spotting, gesture recognition, and activity classification.

Yes. Evolution Infosystem deploys AI systems on NVIDIA Jetson Nano, Jetson Orin NX, Jetson Orin, and Jetson AGX Orin - with TensorRT model optimisation, DeepStream multi-camera pipelines, Docker containerisation, and OTA update systems.

Sub-10ms inference latency for standard vision models (YOLOv8s, MobileNetV3) after TensorRT INT8 optimisation on NVIDIA Jetson Orin NX - enabling real-time production line control at 30-60 FPS.

Yes. All Evolution Infosystem edge AI systems are designed for offline operation - models run entirely on-device, with no cloud API calls required for inference. Network connectivity is used only for model updates and aggregate telemetry transmission.