边缘的AI数据处理降低了成本、数据延迟

网络边缘加速人工智能并减少向云端传输大量数据的需求的竞赛正进行

.

边缘或边缘计算使数据处理资源更接近需要它们的数据和设备,从而减少数据延迟,这对于许多时间敏感的进程非常重要

.

随着边缘设备的数量呈指数级增长,向云端发送大量数据可能会迅速超出预算和宽带能力。这允许设备从非结构化和未标记的数据中进行自学习

网络边缘加速人工智能(AI)并减少向云端传输大量数据的需求的竞赛正进行

这就是人工智能数据处理边缘聚集蒸汽的地方立即访问物联网世界

 

英文译文:

A race is on to accelerate artificial intelligence (AI) at the edge of the network and reduce the need to transmit huge amounts of data to the cloud.

Development of specialized silicon and enhanced machine learning (ML) models is expected to drive greater automation and autonomy at the edge for new offerings, from industrial robots to self-driving vehicles.

But when those models detect something out of the ordinary, they are forced to seek intervention from human operators or get revised models from data-crunching systems. That’s not sufficient in cases where decisions must be made instantaneously, such as shutting down a machine that is about to fail.McKinsey & Co. analysts wrote in a report on AI opportunities for semiconductors. “And that makes the edge, or in-device computing, the best choice for inference.”

Overcoming Budget and Bandwidth Limits

To read the complete article, visit IoT World Today.

 

Share this Post:

相关资讯: