How AI is reshaping the edge computing landscape
What do we mean by “the edge”?
Edge includes any distributed application where specific processing occurs away from the server, even if the data is eventually sent to a data center. The big idea is to avoid sending all the data over the internet for processing on a server and instead allow data to be processed closer to where it's collected, avoiding latency issues with long data roundtrips, and enabling near real-time response on site.
In industrial applications, edge computers are typically designed to take inputs from sensors or other devices and act on the inputs accordingly. For example, preventative maintenance takes acoustic, vibration, temperature, or pressure sensor readings and analyzes them to identify anomalies that indicate slight faults in machines. Machines can be taken offline immediately or when needed to enable maintenance to occur ahead of catastrophic failure. Reaction times must be quick, but data quantity is low. However, AI is putting a strain on these edge systems.
The impact of AI on edge processing loads
Performing complex algorithms on video inputs requires the parallel processing capabilities of power-hungry GPU cards, more memory for efficient and accurate AI inference, and more storage space for additional data. But don't these already exist in data centers?
Bringing a sliver of data center power to the edge
Hardware for the edge
Adding AI at the industrial edge requires hardware suited to the task. An industrial computer that can handle extreme temperatures, vibrations, and space constraints is a must. In particular, three things are needed for vision systems, the most prolific AI application to date, memory to support efficient AI inference, storage for the incoming data, and PoE to support the addition of cameras.
Getting more memory in a smaller space can be accomplished with the latest DDR5. It provides more memory capacity at the edge with higher speeds, with twice the speed and four times the capacity of DDR4 with the same footprint, it makes more efficient use of available space and resources.
Extending capacity is needed for edge applications, as the data must go to the server or stay at the edge for some time, so SSDs are needed for interim storage. The shift from SATA to NVMe has opened the doors to greater speeds and performance and the NVMe PCIe G4X4 SSD is the latest SSD in Cervoz's pipeline, providing the industrial performance for these applications.
Vision systems need cameras. PoE+ is the simplest and most efficient way to add high-speed cameras to the system, providing both power and data transmission through a single cable. Cervoz's upcoming PoE Ethernet Modular PCIe Expansion Card adds this functionality through a small add-on for power.