From medical science, defense, automobile to industrial applications, AI on Edge is taking over the market with its breakthrough abilities. It combines both data from cloud and local storage to give real-time, accurate and efficient inferences which makes it faster, smarter and superior to other cloud or hardware devices.
The regime of AI on Edge is driving AI companies and developers to focus not only on the software development, but also to adopt the right hardware being used in the device. Allied Market Research reports that the hardware market size in Edge AI will grow from 6.8 billion in 2021 to 38.87 billion by 2030.
With the high demand to be foreseen in the near future and many options available in the market, it becomes imperative for the manufacturers and developers to choose the right hardware with required configurations to meet their application needs. In addition to the software needs, one should also consider the best suitable option based on the required efficiency, utility, durability and costs.
In this blog, we will list the different types of hardware required and used in Edge AI devices, their benefits & drawbacks.
1. Central Processing Unit (CPU)
Central Processing Unit or CPU is a chip used in devices to run mixed data inputs, such as systems that use both audio & text, and extract, transform, and load (ETL) processes. It comes with 4-16 cores that can compute parallelly. Because of its lesser number of cores, it falls back processing a large number of data required to run an AI algorithm simultaneously. However, it can be used to train small neural networks but it will take a lot of time. CPU’s are much more energy efficient compared to other Edge AI hardware available. Two major CPU manufacturer names are Intel and AMD.
2. Graphics Processing Unit (GPU)
Graphics processing units or GPUs are used in graphics, virtual reality and videos that require heavy data and floating-point arithmetic processing to carry out functions like drawing geometric shapes, producing large amount of colours, lighting, etc. It has a higher number of smaller cores (100s or 1,000s) compared to CPUs which make them better at multitasking because of their capability to divide large numbers of complex problems into smaller tasks and process them parallelly.
The efficiency in a GPU is also good because of its ability to process large amounts of data specifically to support rendering videos and graphics. However, AI on GPU is not as efficient compared to ASICs which are specifically designed for supporting AI applications. While GPUs have strong computational power, it comes at an expense of energy loss and heat emission that compromises hardware durability.
Some of the notable GPU chips in the market include –
- NVIDIA GeForce RTX 3090 DirectX 12.00
- AMD Radeon 6900 XT DirectX 12.00
- NVIDIA GeForce RTX 3080 Ti DirectX 12.00
- AMD Radeon RX 6800 XT DirectX 12.00
3. Field Programmable Gate Arrays (FPGA)
Field Programmable Gate Arrays (FPGAs) are programmed hardware circuits that enable developers to build a neural network from scratch and tune it according to their needs. FPGAs can run multiple functions parallelly and assign tasks to specific parts of the chip which makes them extremely efficient.
FPGA also comes with low latency and deterministic latency (DL). DL gives out a uniform output from the initial stage with known response time critical for applications with deadlines. This makes execution faster for real-time applications including motion objects, video streaming and speed recognition.
FPGAs are extremely flexible hardware chips. They can be reprogrammed or can be trained for additional capabilities like image processing pipeline without having to use newer hardware. Because of the long product development life cycle (7-10 years), FPGAs are used mostly in the aerospace, medical, industrial and transportation sectors.
Some of the notable FPGA boards in the market include –
- Xilink Spartan 7
- Digilent Basys 3
- Mojo FPGA
- Altera DE2
4. Application Specific Integrated Circuits (ASIC)
Application-specific integrated circuits (ASICs) are designed to perform and operate function-specific tasks in a device and cannot be reprogrammed or used for any other functions other than for what it has been built for. ASICs are typically used in large production devices instead of debugging system devices.
ASIC circuits have a very low power consumption due to their smaller size and their operation oriented characteristics. It is also cheaper to assemble ASICs compared to other hardware because of its simple structure.
Types of ASICs include:
- Vision processing units (VPUs) – Includes image and vision specific processors, and co-processors.
- Tensor processing units (TPUs)- TensorFlow, the first TPU developed by Google for its machine learning framework.
- Neural compute units (NCUs), including those from ARM.
Comparison of different hardware and their characteristics:
| Hardware | Tasks | Efficiency, Power consumption & Durability | Cost |
| CPU | Mixed data inputs, such as systems that use both audio and text, and extract, transform, and load (ETL) processes | – Take time for output
– Less power consumption – Lifespan 5-10 years |
Cost-effective |
| GPU | Well suited for AI workloads, facilitating both neural network training and AI inferencing | – Efficient
– High power consumption and heat layoff – Lifespan of 5 years |
Costlier than CPUs |
| FPGA | Extremely flexible
Can be reprogrammed or you can add additional capabilities like image processing pipeline without going for newer hardware |
– Extremely efficient
– Reduced power consumption – Lifespan of 7-10 years |
Cuts-off additional costs |
| ASIC | Designed for one specific task
Typically used in large production devices instead of debugging system devices |
Efficient
Low power consumption Lifespan of 3-5 years |
More expensive |
Choosing the right Edge AI hardware is very important for an engineered device that works in real-world conditions where there is no room for errors or malfunctioning. For example, choosing the wrong hardware might result in accidents that could cost lives due to their failure in a self-driving car.
The hardware should match your expectations in terms of efficiency, latency, reliability, mobility, and costs. It should be compatible with your software and pack enough processing capabilities to run software and applications flawlessly while performing consistently under different environmental conditions.
