Edge AI Development Lab

EDGE AI ENGINEERING

Edge AI – NPU & TOPS Acceleration

We design and optimize AI systems that run locally on embedded hardware. From model selection to hardware acceleration using NPUs, we enable devices to process vision, sensor and decision workloads without relying on cloud connectivity.

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Edge AI Processing Diagram

What is Edge AI?

Edge AI refers to running artificial intelligence algorithms directly on the device where data is generated — such as cameras, machines, robots, or IoT controllers — instead of sending data to remote servers.

In industrial and real-time environments, cloud processing introduces latency, network dependency, and data-security risks. Edge AI allows immediate decision-making inside the device itself.

This is achieved by using specialized hardware accelerators like NPUs (Neural Processing Units) capable of executing deep learning models efficiently.

• No internet dependency
• Millisecond decision making
• Data privacy (no video upload to server)
• Lower operational cost
• Continuous operation in factory environments

Our Approach to Edge AI System Development

We engineer complete deployable systems — from feasibility analysis to optimized on-device deployment on embedded hardware.

Edge AI Development Workflow

Problem Definition

Understanding the application, constraints and feasibility of running AI directly on the target hardware device.

Dataset Planning

Designing data collection strategy, capturing real environment samples and preparing labeled datasets for training.

Model Training & Validation

Training deep learning models and validating performance using real-world test conditions and evaluation metrics.

Embedded Optimization

Optimizing the model using quantization, pruning and memory reduction to fit embedded hardware constraints.

NPU Acceleration

Deploying the model on hardware accelerators such as NPUs to achieve real-time inference performance.

On-Device Deployment

Integrating the AI model with camera or sensors and running inference directly on the device without cloud dependency.

Edge AI Processing Fundamentals

NPU and TOPS together define how efficiently a system can execute neural network inference workloads.

Neural Processing Unit (NPU)

Dedicated accelerator designed for neural network inference such as object detection, image recognition and real-time AI processing directly on the device.

Compute Throughput (TOPS)

Represents how many AI operations the processor can execute per second. Higher TOPS enables faster AI models, smoother vision tasks and parallel AI workloads.

Power Efficiency & Thermal

NPU executes AI workloads using significantly lower power than CPU/GPU, enabling sustained AI performance without overheating or throttling.

What We Actually Do

Engineering Services We Provide for AI, NPU & Edge Intelligence Systems

1

Model Optimization

Quantization (FP32 → FP16 / INT8), pruning & compression, TensorRT / OpenVINO / TFLite conversion and latency optimization.

2

Hardware Bring-Up

Camera & sensor integration, driver debugging and memory pipeline tuning.

3

On-Device Deployment

Running inference on NPU, multi-threaded pipelines and real-time frame handling.

4

Performance Benchmarkting

FPS measurement, latency analysis, thermal stability and long duration runtime testing.

Use Cases

Real-World Use Cases of Edge AI

Real-world deployment scenarios where on-device intelligence delivers measurable value across multiple industries.

Industrial Inspection

Industrial Inspection

Detect manufacturing defects instantly using real-time visual inspection systems.

Industrial Inspection

Smart Surveillance

Intrusion detection, people counting and restricted-area alerts without cloud storage.

Industrial Inspection

Robotics

Robots perceive surroundings, identify objects, and navigate autonomously safely.

Industrial Inspection

Retail Analytics

Track customer movement, queues, and shelf activity inside stores.

Industrial Inspection

Agriculture Systems

Monitor crops, detect diseases, and automate irrigation decisions intelligently.

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