EndurEdge
The number of connected devices collecting data is continually expanding. This requires more storage and computational capacity and more Artificial Intelligence (AI) to be brought at the Edge. EndurEdge is an Edge AI software that combines rugged embedded and Edge computers, computational power, and IoT components to enable Edge AI. By bringing these high-performance computing capacities to the Edge, EndurEdge enables Artificial Intelligence (AI) applications directly on field devices. They are able to process data autonomously and perform Machine Learning (ML) in the field and apply Deep learning (DL) models and algorithms for advanced autonomous applications, such as Automated Invoicing and self-guided expense processing. The virtually unlimited capacity of the Cloud can be integrated with sophisticated and high-performance Edge Computers in the field, enabling the “Intelligent Edge”. Data are processed in real-time and filtered to be sent to the cloud for additional analytics, integrating OT (operational technology) with IT (Information Technology) applications.
Edge AI means that AI software algorithms are processed locally on a hardware device. The algorithms are using data (sensor data or signals) that are created on the device. A device using Edge AI software does not need to be connected in order to work properly, it can process data and take decisions independently without a connection. Edge computing can connect large-scale servers and services to individual devices.
Benefits of on-device intelligence using EndurEdge
EndurEdge enable organisations to increase their ability to not only collect data from connected devices, but also analyse the data and drive data-driven decision making, while avoiding the cost, complexity, and security challenges associated with storing data on the cloud. The cloud however remains critical and cloud-based AI will continue to complement on-device processing for pooling of big data and training results for many AI inference algorithms running on the device.
EndurEdge use cases
EndurEdge is designed to run on the majority of the consumer edge AI chips that are in high-end smartphones, they are accounting for more than 70% of all consumer edge AI chips currently in use. With the growth of edge AI across applications NVIDIA, Apple, and multiple other emerging startups are building chips exclusively for AI workloads at the edge. Further, a convergence of several overlapping technology trends including IoT, computer vision and robotics, is making new usages possible.
These use cases not only help improve quality of life and business but also help solve problems being faced by consumers and businesses today. The rise of edge AI chips is expected to drive significant differences for both consumers and enterprises. For consumers, edge AI chips will unlock incredible possibilities that they will be able to leverage without an internet connection on their smartphones. However, long term, greater impact of edge AI chips is expected from the use in enterprises, enabling organisations to take their IoT applications to the next level.
The need for real-time decision making is pushing AI closer to “the edge”, giving devices the ability to process information and accelerate machine learning tasks locally. Deloitte predicts that in 2020, more than 750 million edge AI chips are expected to be sold. Further, by 2024, the sales of edge AI chips are expected to exceed 1.5 billion, representing more than 20% annual unit sales growth. The reason behind this rapid growth lies in the fact that edge AI chips are increasingly finding their way into consumer market, in addition to enterprise edge devices.
The need for real-time decision making is pushing AI closer to “the edge”, giving devices the ability to process information and accelerate machine learning tasks locally. Deloitte predicts that in 2020, more than 750 million edge AI chips are expected to be sold. Further, by 2024, the sales of edge AI chips are expected to exceed 1.5 billion, representing more than 20% annual unit sales growth. The reason behind this rapid growth lies in the fact that edge AI chips are increasingly finding their way into consumer market, in addition to enterprise edge devices.
