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https://neuralnetlab.com/wp-content/plugins/dmca-badge/libraries/sidecar/classes/{"id":529,"date":"2021-09-12T15:20:06","date_gmt":"2021-09-12T15:20:06","guid":{"rendered":"https:\/\/neuralnetlab.com\/?p=529"},"modified":"2021-09-14T20:09:25","modified_gmt":"2021-09-14T20:09:25","slug":"edge-ai","status":"publish","type":"post","link":"https:\/\/neuralnetlab.com\/edge-ai\/","title":{"rendered":"Edge AI (Deep Neural Networks for Edge AI)"},"content":{"rendered":"\n
Deep Neural Networks (DNNs) are the key technology of enabling AI. It has gained widespread attention which is now growing at a rapid pace. But, running computation-intensive DNN-based tasks on mobile edge devices can be challenging due to the limited computation resources. This is one of the challenges presented with Edge AI<\/strong>.<\/p>\n\n\n\n Therefore the traditional cloud-assisted AI inference has been the preferred choice but is heavily affected by the wide-area network (WAN) latency, leading to poor real-time performance, hence ultimately causing poor user experience. Moreover, sending data over the internet to cloud-based servers has raised many privacy and security issues over time.<\/p>\n\n\n\n This is where the concept of Edge AI<\/strong> excels. By pushing inference and occasionally even model training to edge nodes, Edge AI has recently emerged as a promising alternative to traditional cloud-assisted inference. The concept of Edge AI defines that the inference is taken place where data is collected or at the closest point locally such as on a local server.<\/p>\n\n\n\nWhat exactly is Edge AI?<\/h2>\n\n\n\n