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What is Edge AI?

Put simply, edge AI is the combination of Edge Computing and AI. This technology is becoming pervasive in a growing number of applications across a multitude of industry verticals for example:

Smart AOI

Public Safety


Medical Imaging

ITS/Traffic monitoring

Edge AI refers to the deployment of artificial intelligence (AI) technologies and capabilities directly on edge devices, such as smartphones, Internet of Things (IoT) devices, embedded systems, and other local computing devices. Unlike traditional AI systems that rely on cloud computing and remote servers for processing and analysis, edge AI brings the power of AI algorithms and machine learning models closer to the data source, enabling real-time processing and decision-making at the edge of the network.

The main idea behind edge AI is to reduce latency, enhance privacy, increase efficiency, and enable intelligent applications that can operate offline or with limited network connectivity. By processing data locally on the edge device, edge AI systems can quickly analyze and respond to data without relying on a constant internet connection or transmitting sensitive data to the cloud. This is especially beneficial in scenarios where low latency and real-time decision-making are critical, such as autonomous vehicles, industrial automation, healthcare monitoring, and smart home devices.

Edge AI leverages various technologies to achieve its objectives. This includes deploying lightweight and optimized AI models that can run efficiently on resource-constrained devices. Techniques like model compression, quantization, and pruning are employed to reduce the model size and computational requirements without significant loss in performance. Additionally, edge AI often utilizes specialized hardware accelerators, such as graphics processing units (GPUs), field-programmable gate arrays (FPGAs), or dedicated AI chips, to enhance processing capabilities and energy efficiency.

The applications of edge AI are diverse and rapidly expanding. In the healthcare domain, edge AI can enable wearable devices to monitor vital signs and provide real-time health insights. In smart cities, edge AI can help analyze video feeds from surveillance cameras to detect anomalies or manage traffic flow. In agriculture, edge AI can enable intelligent systems that monitor crop health and optimize irrigation. Edge AI is also instrumental in enabling intelligent personal assistants, augmented reality experiences, and real-time translation on mobile devices.

Overall, edge AI empowers devices at the edge of the network with advanced AI capabilities, enabling them to process and analyze data locally. By reducing reliance on the cloud and enabling real-time decision-making, edge AI unlocks new opportunities for innovation, privacy, efficiency, and enhanced user experiences across various industries and domains.

Source: Edge AI and Vision Alliance, Computer Vision Developer Survey, 2022
© 2022 Edge AI and Vision Alliance – Used with Permission


Tritech Solutions offers a number of systems targeting Edge AI workloads and applications:



We also have a wide range of boards, modules and subsystems based on different Edge AI platforms:



A new product offering from our partner Innodisk is cameramodules for embedded vision applications:




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