Edge AI services
Edge Artificial Intelligence (edge AI) is transforming the landscape of technology by enabling artificial intelligence processes to occur directly on devices, rather than relying on cloud-based platforms. Witekio experts can help you implement edge AI applications in your embedded systems, enhancing performance and autonomy while reducing dependency on continuous internet connection or bandwidth.
How we can help deploy your Edge AI solutions
Production-ready BSP development
We develop production-ready Board Support Packages (BSPs) to ensure high-performance and seamless AI integration into IoT devices. Our BSP solutions can also leverage hardware acceleration features such as GPUs, NPUs, and DSPs, optimizing AI inference for real-time processing.
Cloud connectivity & data collection
Efficient cloud connectivity is crucial for model updates and data synchronization in Edge AI systems. We enable secure data collection from edge devices, allowing AI models to be trained, updated, and enhanced through cloud-based transfer learning and federated learning techniques.
ai model integration & framework support
We integrate AI models from leading frameworks, including NVIDIA TAO Toolkit, TensorFlow Lite, PyTorch Mobile, Intel OpenVINO, Edge Impulse, and more. We ensure AI models are efficiently deployed on embedded systems by optimizing hardware utilization, leveraging hardware accelerators, and configuring runtime environments to improve inference performance.
peripheral & sensor software support
Edge AI applications rely on diverse sensors and peripherals for data input. We support you with:Camera integration for real-time computer vision processing Microphone and audio processing for voice-based AI Sensor fusion to combine multiple data sources for better AI decision-making
security enhancements for edge AI
Claude Pettinato
Head of Software Development
The team fully met our needs and urgent requirements, as well as effectively controlled the budget and project activities.
We’ve been working with Witekio for two years. The quality of the deliverables gets better every time.
We’ve been working with Witekio for two years. The quality of the deliverables gets better every time.
Our approach to implement Edge AI technology in your systems
1.
Assessment & planning
We begin by evaluating your current systems and identifying opportunities for integrating Edge AI. This involves understanding your specific needs, hardware devices and capabilities, and the potential benefits of local AI processing.
2.
Hardware & software integration
We select and integrate the appropriate hardware components, such as CPUs, GPUs, or specialized AI chips, to support efficient Edge AI operations. Additionally, we ensure that software frameworks and tools are optimized for your system.
3.
Development & customization
Our team customizes AI models tailored to your application’s requirements. This phase includes, minor modifications of the model’s architecture, model training with relevant data and adaptation of the model (like quantization) for effective inference on edge devices.
4.
Deployment & support
We oversee the deployment of the Edge AI solution and provide ongoing support to ensure its smooth operation. This includes managing updates, troubleshooting issues, quality control and optimizing performance based on real-world usage.
Benefits of Edge AI
Scalability
Edge AI models offers a highly scalable solution by processing data locally, reducing the dependency on centralized cloud infrastructures.
Data security
With edge devices operating independently of cloud networks, the risk of large-scale cyberattacks is minimized.
Minimized latency
Edge AI enables near-instantaneous data processing by eliminating the need to transmit data to the cloud for initial analysis. This reduction in latency allows for real-time decision-making, critical in time-sensitive applications.
Cost efficiency
By reducing the volume of data transferred to the cloud, edge AI conserves bandwidth and decreases the need for extensive cloud storage and processing resources.
Applications of Edge technology in embedded systems
Healthcare
Edge computing is revolutionizing healthcare by enabling real-time analysis of medical imaging, which assists in swift diagnoses and treatment planning. Processing sensitive health data locally ensures privacy and compliance with regulatory standards.
Smart devices
Smart devices like home assistants and appliances benefit from edge AI by processing data directly on the device. This minimizes the need for cloud-based interactions, safeguarding user privacy and improving the response speed for user commands, thus elevating the overall experience.
Surveillance & monitoring
In surveillance systems, edge AI allows to instantaneously process data from the video feed of security cameras. Running AI algorithms on a edge device ensures detection and response to security incidents without relying on cloud infrastructure, maintaining functionality even during network disruptions.
Industrial IoT
In industrial IoT, edge computing enhances processes such as predictive maintenance, quality assurance, and real-time monitoring. Deploying AI at a edge device reduces data processing latency and boosts operational efficiency by performing analytics directly on factory floor devices.
We are Edge AI experts
At Witekio, we support your edge AI implementation, offering device optimizations for long-term use and ensuring high availability through advanced AI solutions.
4 countries
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