Edge AI, also known as edge intelligence, is a term that refers to the convergence of two dynamic technological trends: edge computing and Artificial Intelligence. In today’s tech landscape, the proliferation of mobile devices and advancements in communication technologies have fueled the rise of edge computing, which focuses on processing data at the network edge. 

Simultaneously, AI has made remarkable strides in the last half-decade, particularly in deep learning and hardware improvements, enabling powerful AI applications. The combination of these forces results in Edge Intelligence. 

A report by Fortune Business Insights projects the market for edge AI to grow to USD 107.47 billion by 29, with a CAGR of 31.7% during 2023-2029. In recent years, applications and use cases of edge AI have found the most results in the automotive, healthcare, and manufacturing sectors. Edge AI also seems to be promising for cybersecurity while safeguarding sensitive data. 

The surge in edge AI use cases is also driving adoption for 5G networks and IoT-based edge computing solutions to connect IT and telecom. While the demand for edge AI and its application is increasing, so are the business leaders’ questions on what hardware and infrastructure the company needs to deploy edge AI across the board. 

Emerj Senior Editor Matthew DeMello recently spoke with the leaders from Rain, Intel, GE Research, and Groq on the ‘AI in Business’ podcast in the special series – Beyond GPU to discuss the hardware and infrastructure requirements for edge AI. 

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