Edge computing involves processing computations on remote devices with limited processing capabilities and no internet connectivity.
FREMONT, CA: Researchers are striving hard to shape the technological future in a way which is both efficient and affordable. Though various technologies have shaped our lives and industries in multiple ways, the emergence of artificial intelligence (AI) has significantly changed the capabilities of the edge devices.
Deep Learning and machine learning (ML) along with increased computing power, are making edge devices smarter than ever. The devices can provide insights and real-time predictive analysis, for instance, a small embedded device on a lamp post can detect when a vehicle is speeding.
Sophisticated ML models embedded at the edge will impact speech synthesis, video frames, and time-series unstructured data generated by cameras, sensors, and microphones. Thus photography, video shooting, and voice assistants are getting smarter and effective. As more and more devices are using technologies like cloud and AI, it is imperative to simultaneously gear up the supporting devices according to the transformation, and that's where edge computing fits in.
Companies are increasingly moving toward solutions that specialize in “edge computing,” which involves processing computations on remote devices with limited processing capabilities and no internet connectivity rather than on a central server or even a PC. The vision is to incorporate AI into devices such as security cameras, traffic lights, home appliances, and also the odd space probe.
There lie tremendous capabilities in AI, but its real-life applications are still limited because the algorithm requires loads of electricity and computational capabilities. The challenges to introduce AI in actual products are many such as price, power, and dealing with exhausts. However, edge computing promises to spread AI inside the various smaller machines and gadgets in people’s offices and homes.
Edge computing aims to offset the numbers so that smaller chips can process huge chunks of data without losing too much precision. It's a significant challenge as for each digit that is lopped off there is an exponential loss in the ability to express the clear picture. That is the reason why even a small achievement is treated as a significant gain in the arena of edge computing.
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