Janakiram MSV Contributor
AI has become the key driver for the adoption of edge computing. Originally, the edge computing layer was meant to deliver local compute, storage, and processing capabilities to IoT deployments. Sensitive data that cannot be sent to the cloud for processing and analysis is handled by the edge. It also reduces the latency involved in the roundtrip to the cloud. Most of the business logic that runs in the cloud is moving to the edge to provide low-latency, faster response time. Only a subset of the data generated by sensors is sent to the cloud after aggregating and filtering the data at the edge. This approach significantly saves the bandwidth and cloud storage costs for enterprises.
Edge is delivering three essential capabilities - local data processing, filtered data transfer to the cloud and faster decision making.
Since most of the decision making is now taking advantage of artificial intelligence, the edge is becoming the perfect destination for deploying machine learning models trained in the cloud.
Machine learning and deep learning models are exploiting the power of Graphics Processing Units (GPU) in the cloud to speed up training. Mainstream cloud providers such as AWS, Azure, Google Cloud Platform, IBM, and Alibaba are offering GPU as a service. Modern deep learning frameworks including TensorFlow, PyTorch, Apache MXNet and Microsoft CNTK are designed to take advantage of the underlying GPUs for accelerating the training process.
When compared to the enterprise data center and public cloud infrastructure, edge computing has limited resources and computing power. When deep learning models are deployed at the edge, they don’t get the same horsepower as the public cloud which may slow down inferencing, a process where trained models are used for classification and prediction.
To bridge the gap between the data center and edge, chip manufacturers are building a niche, purpose-built accelerators that significantly speed up model inferencing. These modern processors assist the CPU of the edge devices by taking over the complex mathematical calculations needed for running deep learning models. While these chips are not comparable to their counterparts - GPUs - running in the cloud, they do accelerate the inferencing process. This results in faster prediction, detection and classification of data ingested to the edge layer.