Edge AI

Being able to process AI functions on the 'Edge', will make it increasingly useful and visible.  Some details about the challenge.

Exploring Artificial Intelligence at the Edge  By Bruce Kornfeld in DataNami

As the adoption of artificial intelligence (AI), deep learning, and big data analytics continues to grow, it is becoming increasingly important for edge computing systems to process large data sets in a timely and efficient manner. The basic compute, storage and networking capabilities are all present today at the edge, but speeds and capacity will only continue to increase and advancements like NVMe (Non Volatile Memory Express) will offer significant performance advantages and boost AI adoption at the edge.

Edge-based AI: Are We There Yet?
It is possible, and becoming easier, to run AI and machine learning with analytics at the edge today, depending on the size and scale of the edge site and the particular system being used.

While edge site computing systems are much smaller than those found in central data centers, they have matured, and now successfully run many workloads due to an immense growth in the processing power of today’s x86 commodity servers. It’s quite amazing how many workloads can now run successfully at the edge.

For example, many large retailers are using edge computing solutions today because it is cost-prohibitive to send data to the cloud for processing, and the cloud is not able to keep up with retailers real-time demands. They are running local analytics applications as well as AI algorithms at these edge sites.

While the basic compute, storage, and networking capabilities are “there” today, we anticipate they will continue to improve over time to allow for more workloads to run successfully at the edge. Processing speeds and storage capacities will continue their torrid pace.

For instance, one advancement that is making its way to the edge is NVMe . This new protocol offers significant performance advantages for solid state disks (SSDs) since they communicate directly on the PCIe bus. Legacy spinning disk drives primarily use the SATA interface, which is much slower and designed for performance characteristics of spinning disks and not for the “new age” storage of flash memory (used within SSDs).  .... "