Estimates in the 2021 State of the Edge Report by the Linux Foundation put edge infrastructure’s value at $800 billion by 2028. This number shows the immense growth in the edge computing sector. Not only that, but edge computing is continually becoming more tightly integrated with its cousin, cloud computing.
With so much investment going towards edge infrastructure improvements, companies need to make sure that every dollar counts. This article will look at five different processor types crucial to efficient, optimized edge computing and where they excel.
1. CPUs Are the Industry Standard
In most computing devices, x86 and arm CPUs are the standard computing chip. High-powered chips like the Intel Xeon processor are designed specifically for the needs of edge computing. These chips allow users to get superior performance while carefully controlling power consumption. CPUs are the most common and streamlined of all the chips on this list, making them especially easy to source and lowering their upfront cost.
2. GPUs Provide Superior Parallel Computing Performance
Graphics processing units (GPUs) are far superior at repetitive and highly parallel computing tasks due to the difference in structure compared to a CPU. In a CPU, there are a limited number of cores (typically 4-8) that provide enhanced performance. However, there are hundreds to thousands of cores in a GPU, which, while not as intelligent or as flexible as CPU cores, provide massive performance upgrades for the desired type of work. Many server environments opt to get the best of both worlds by using CPUs and GPUs in tandem to offer superior performance. Common applications include deep learning, AI, and image processing.
3. VPUs Are Targeted Towards Object Recognition and Machine Learning
The vision processing unit (VPU) is a specialized process suitable for running different types of machine vision algorithms. Like GPUs, they are geared toward image processing, but their specialized nature makes them more efficient than their counterparts. VPUs also don’t contain specialized hardware for rasterization and texture mapping for 3D graphics. Therefore, efficiency comes at the cost of flexibility in use cases.
4. ASICs Are Application-Specific and Highly Efficient
Application-specific integrated circuits (ASICs) differ from the other processors on this list in that they are customized for a particular use. For example, if someone were to create an ASIC for bitcoin mining, it would only be used for that task and cannot be changed. Despite their inflexible nature, ASICs have many advantages. Because they are single purpose, all of the circuitry on the chip is dedicated to the task at hand, which lowers the cost of production since you get more performance out of every chip. This simplicity also makes the chips easier to produce and makes them more energy efficient. It’s clear that unchanging jobs could see considerable gains from using an ASIC chip.
4.5. ASSPs Are a Standardized ASIC
Application-specific standard products (ASSPs) are integrated circuits designed and implemented like ASICs. The key differentiator is that ASSPs are a standard product, so they are not made for a single customer. Although, like ASICs, they can only perform the tasks they were designed to execute.
5. FPGAs Allow Users To Reprogram at Will
Field-programmable gate arrays (FPGAs) are the antithesis of ASICs in that there is no chip, and they are entirely programmable. The advantage of FPGAs is that they can be programmed to specific workloads so that they require less power while increasing performance. Additionally, when the job or workload changes, they can be reprogrammed to the new job. These two factors often improve the total cost of ownership for users.
Which Processor Is Right for Your Edge Hardware Needs?
As we’ve seen, each processor has its advantages and disadvantages. While FPGAs have superior flexibility, the difficulty of setup and deployment could make businesses opt for a more streamlined processor. Additionally, the application-specific efficiency is a major selling point for ASIC-powered hardware. The same is true of VPUs, which are great for running machine vision applications. Finally, the streamlined nature and compatibility of CPU and GPU hardware make them the go-to for much of the edge infrastructure we see today. If you’re looking to build edge infrastructure that helps you enhance your network, Intequus can help. Our team is adept at optimizing hardware to work in harmony so that you can get the most out of every dollar spent. Talk about your edge hardware needs with a team member today.