Edge computing is about computing locations closer to the application than the cloud. However, is that 300 miles, 3 miles or 300 feet? In the world of computing, the cloud theoretically has infinite memory and infinite compute power. At the device, there is theoretically just enough compute and memory resources to capture and send data to the cloud. Both theoreticals are a bit beyond reality but let’s use this as a method to describe the different levels of edge compute. As the cloud computing resources get closer to the end point device or application, theoretically, the storage, memory and computing resources become less and less. The power that is consumed by these resources is also lowered. The benefits of moving closer not only lower the power but lower the latency and increase the efficiency.
Three basic edge computing architectures are starting to emerge within the space (Figure 6). First and closest to traditional data centers are regional data centers that are miniature versions of cloud compute farms placed strategically to reduce latency but maintain as much of the compute, storage and memory needed. Many companies and startups address this space but SoCs designed specifically to address regional data centers do little to differentiate from classic cloud computing solutions today, which focus on high-performance computing (HPC).
Local servers and on-premise servers, the second edge computing segment, are where many SoC solutions address the power consumption and connectivity needs of edge computing specifically. There is also a large commercialized development on software today, in particular with the adoption of more flexible platforms that enable containers such as Dockers and Kubernetes. Kubernetes is used in the Chick-Fil-A example described earlier. The most interesting piece of the on-premise server segment with respect to semiconductor vendors are the advent of introducing a chipset adjacent to the server SoC to handle the AI acceleration needed. Clearly an AI accelerator is located in the compute farms in the cloud, but a slightly different class of AI accelerator is built for the edge servers because this is where the market is expected to grow and there is opportunity to capture a foothold in this promising space.
A third segment for edge computing includes aggregators and gateways that are intended to perform limited functions, maybe only running one or a few applications with the lowest latency possible and with minimal power consumption.
Each of these three segments have been defined supporting real world applications. For instance, McKinsey has identified over 107 use cases in their analysis of edge computing4. ETSI, via their Group Specification MES 002 v.2.1.1, has defined over 35 use cases for 5G MEC including for gaming, service level agreements, video caching, virtual reality, traffic deduplication, and much more. Each of these applications have some predefined latency requirements based on where in the infrastructure the edge servers may exist. The OpenStack Foundation is another organization that has incorporated Edge Computing into their efforts with Central Office ReArchitected as a Data Center (CORD) latency expectations where traditional telecom offices distributed throughout networks are now hosting edge cloud servers.
The 5G market expects use cases as low as 1ms latency roundtrip, from the edge device, to the edge server, back to the edge device. The only way to achieve this is through a local gateway or aggregator, as going all the way to the cloud typically takes 100ms. The 6G initiative, which was introduced in the fall of 2019, announced the goal for 10s of µS latency.
Each of the edge computing systems support a similar architecture of SoCs that include a networking SoC, some storage, a server SoC, and now an AI accelerator or array of AI accelerators. Each type of system offers its own levels of latency, power consumption, and performance. General guidelines for these systems are described in Figure X. The market is changing and these numbers will likely move quickly as the technology advances.