The network edge's role in Artificial Intelligence (AI) inference processing is rapidly expanding, driven by a plethora of applications seeking computational advantages. These applications strive for data-driven efficiency, leveraging robust AI capabilities and prioritizing real-time responsiveness. However, as demand grows, so does system complexity. The proliferation of AI inference accelerators showcases innovation but also underscores challenges, particularly the varied software and hardware configurations of these devices. This diversity, while advantageous for certain tasks, introduces hurdles in device integration and coordination. In this paper, our objectives are three-fold. Firstly, we outline the requirements and components of a framework that accommodates hardware diversity. Next, we assess the impact of device heterogeneity on AI inference performance, identifying strategies to optimize outcomes without compromising service quality. Lastly, we shed light on the prevailing challenges and opportunities in this domain, offering insights for both the research community and industry stakeholders.

A key benefit of using general-purpose processors to implement open RAN/vRAN is that the same platforms can be used to support AI inference and other applications at the far edge of the network, such as cell site routers (CSRs) and content delivery and hosting. These edge platforms can be used to host virtualized applications closer to the user, offering significant benefits in terms of lower latency and shared infrastructure. To find out more about which applications service providers plan to support on shared far edge solutions and how they plan to deploy open RAN and vRAN platforms and architectures for 5G networks, Heavy Reading ran an exclusive survey of individuals working for operators with mobile network businesses. The results are presented in an analyst report, Open RAN Platforms and Architectures Operator Survey Report, that can be downloaded for free here. The survey presented options for five edge applications that can share server platforms with virtualized open RAN baseband implementations.

Bringing convolutional neural networks (CNNs) to your industry--whether it be medical imaging, robotics, or some other vision application entirely--has the potential to enable new functionalities and reduce the compute requirements for existing workloads. This is because a single CNN can replace more computationally expensive image processing, denoising, and object detection algorithms. However, in our experience interacting with customers, we see the same challenges and difficulties arise as they move an idea from conception to productization. In this article, we'll review the common challenges and address some of the solutions that can smooth over development and deployment of CNN models in your edge AI application. We see a lot of companies attempting to create models from the ground up.

While AI originally was targeted for data centers and in the cloud, it has been moving rapidly towards the edge of the network where it is needed to make fast and critical decisions locally and closer to the end user. Sure, training can be still done in the cloud, but in applications such as autonomous driving, it is important that the time-sensitive decision making (spotting a car or pedestrian) is done closer to the end user (the driver). After all, edge systems can make decisions on images coming in at up to 60 frames per second, enabling quick actions. These systems are made possible through edge inference accelerators that have emerged to replace CPUs, GPUs and FPGAs at much higher throughput/$ and throughput/Watt. The ability to do AI inferencing closer to the end user is opening up a whole new world of markets and applications.

While AI originally was targeted for data centers and in the cloud, it has been moving rapidly towards the edge of the network where it is needed to make fast and critical decisions locally and closer to the end user. Sure, training can be still done in the cloud, but in applications such as autonomous driving, it is important that the time-sensitive decision making (spotting a car or pedestrian) is done closer to the end user (the driver). After all, edge systems can make decisions on images coming in at up to 60 frames per second, enabling quick actions. These systems are made possible through edge inference accelerators that have emerged to replace CPUs, GPUs and FPGAs at much higher throughput/$ and throughput/Watt. The ability to do AI inferencing closer to the end user is opening up a whole new world of markets and applications.

The ability to do AI inferencing closer to the end user is opening up a whole new world of markets and applications. While AI originally was targeted for data centers and in the cloud, it has been moving rapidly towards the edge of the network where it is needed to make fast and critical decisions locally and closer to the end user. Sure, training can be still done in the cloud, but in applications such as autonomous driving, it is important that the time-sensitive decision making (spotting a car or pedestrian) is done closer to the end user (the driver). After all, edge systems can make decisions on images coming in at up to 60 frames per second, enabling quick actions. These systems are made possible through edge inference accelerators that have emerged to replace CPUs, GPUs and FPGAs at much higher throughput/$ and throughput/Watt.