Bringing AI To Edge Devices

Author(s): Lalit Kumar

Originally published on Towards AI.

Running Neural Networks Locally

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In recent years, the field of artificial intelligence (AI) has witnessed tremendous advancements that is revolutionizing various industries and transforming the way we work. There is a strong demand to bring AI to the Edge Devices due as it is more practical. Such technology that brings AI capabilities directly to the edge devices, enabling real-time, localized intelligence without relying on constant connectivity to the cloud is the future of AI. We can see smart connected devices around us like ACs, Refrigerators, TVs etc. Many of these devices are already running some sort of machine learning algorithms in them to act smartly. But there is still a huge gap as to how capable these devices could be and with more capabilities can assist us better. After the amazing results we have achieved with Deep Neural Networks, there is a huge push to add capabilities in the smart IoT devices to locally run DNNs. In this article, I aim to provide an introductory overview of Edge AI, its significance, applications, and the current technological landscape enabling NNs on the Edge.

Edge AI refers… Read the full blog for free on Medium.

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Published via Towards AI