The Edge Is Where the Action Is!

Seamless, cloud-connected, and always secure personalized experiences that enable your operations at the edge.

Signal Analysis and Identification - Redefining Compute at the Edge

Terabytes of data are being generated from enterprise digital edges such as vehicles, planes, facilities and infrastructure. The manipulation of these data sources at the edge can enable insights and intelligence to improve operations and decision-making.Real-time analytics are a required component for situational awareness at the tactical edge. Real-time signal identification, using digital techniques and deep learning at the edge, along with the ability to re-train the neural network for new signals/anomalies is a capability not normally associated with Edge

Adding complexity: cyber adversaries

Any wireless interface – from Wi-Fi access points, to a smartphone’s Bluetooth® interface, or even from wireless security camera backhaul connections – is a potential attack vector for cyber adversaries.

The wireless communications domain is a complex world with thousands of signals – generated by both good and bad players. There is a constant need for monitoring the wireless world, for both confirming known good signals and being able to detect anomalous signals/rogue waveforms.

Leveraging artificial intelligence at the edge

Artificial intelligence (AI) computing at the edge solves problems for both the commercial wireless world in addition to supporting defense applications worldwide. This solution is centered around near real-time signal identification on a SWaP-optimized Edgeline EL8000. AI computing at the edge means utilizing deep learning neural networks and GPU-accelerated computing on an enterprise platform that is easily deployable and field ready.

Attend this session to learn about:

The powerful HPE Edgeline EL8000 Converged Edge System, which provides Enterprise class computing, security and manageability, optimized for Size, Weight, and Power (SWaP). The HPE Edgeline EL8000 Converged Edge System enables the processing of vast amounts of data in real time directly at the edge, based on open standards to boost flexibility and reduce costs. Now, it is possible to deploy a new neural network with newly identified user-specific capabilities in the field – without having to return to the datacenter.