Artificial Intelligence Hardware: From CPUs and GPUs, to TPUs, VPUs and FGPAs

Artificial Intelligence Hardware: Overview

The hardware required to support artificial intelligence (AI) can vary depending on the specific  application and the type of AI being used. Some AI algorithms are computationally intensive and require powerful hardware to run effectively.

Deep learning algorithms, which are used for tasks such as image and speech recognition, typically require specialised hardware such as graphics processing units (GPUs) to run efficiently. GPUs are designed to perform the complex matrix calculations that are required for deep learning – and they can be much faster than traditional central processing units (CPUs) at these types of tasks.

Other types of AI, such as rule-based systems, may not require as much computational power and can be run on standard CPUs.

In addition to the type of hardware required, the amount of hardware needed will depend on the scale of the AI application. For example, a large-scale machine learning system that processes a large amount of data may require a cluster of servers with multiple CPUs and GPUs, while a smaller-scale system may only require a single CPU or GPU.

In general, the hardware requirements for AI applications will depend on the specific requirements of the application and the type of AI being used. It’s important to carefully consider the hardware requirements when planning an AI project to ensure that the system has the necessary computational power to run effectively.

Artificial Intelligence Hardware: From CPUs and GPUs, to TPUs, VPUs and FGPAs

In the context of artificial intelligence, CPUs (central processing units), GPUs (graphics processing units), TPUs (tensor processing units) and VPUs (vision processing units) are all types of hardware that are used to perform different types of computations, namely :-

• CPU’s are general-purpose processors that are found in almost all computers. They are designed to handle a wide range of tasks, including running operating systems, executing instructions and performing basic mathematical operations. In AI applications, CPUs can be used to perform tasks such as pre-processing data and running simple machine learning algorithms.
• GPU’s are specialised processors that are designed to handle the complex calculations required to render graphics and images. They are used in computers to improve the performance of tasks such as video rendering and gaming. In AI applications, GPU’s can be used to accelerate the training of machine learning models, as they are able to perform many calculations simultaneously. This makes them well-suited for tasks such as training deep learning models, which require a large number of matrix calculations.
• TPU’s are specialised processors that are designed specifically for machine learning and AI applications. They are used to accelerate the training and inference of deep learning models, and are optimised for the matrix calculations that are commonly used in these types of tasks. TPU’s are used in Google’s cloud computing platform and are available to developers through the Google Cloud AI Platform.
• VPU’s or vision processing units are a type of hardware that are specifically designed to accelerate the processing of visual data. They are often used in artificial intelligence applications that involve tasks such as image recognition, object detection and video analysis. VPU’s are similar to GPU’s (graphics processing units) which are also used to accelerate the processing of visual data. However, VPU’s are typically optimised specifically for vision-based tasks and may offer additional features such as support for multiple cameras and real-time image stitching.
• FPGA’s (Field Programmable Gate Arrays) can be used to accelerate the processing of data and the training of machine learning models. They are often used in applications where real-time processing is required, such as autonomous vehicles and robotics. An advantage of FPGA’s are they can be reprogrammed to perform different tasks, which make them flexible and adaptable. They can also be more energy-efficient than CPU’s and GPUs, being tailored specifically for the task at hand.

Artificial Intelligence Hardware: Accelerators

AI accelerators are specialised hardware devices that are designed to accelerate the processing of artificial intelligence (AI) applications. They are used to improve the performance of AI tasks such as machine learning and deep learning and can be an important component of an AI system.

There are several types of AI accelerators, including graphics processing units (GPU’s), tensor processing units (TPU’s) and field-programmable gate arrays (FPGA’s). These accelerators are designed to perform specific types of computations that are commonly used in AI applications and are much faster at these tasks than traditional CPU’s (central processing units).

AI accelerators are used in a variety of applications, including image and speech recognition, natural language processing and predictive analytics. They are often used in conjunction with other types of hardware, such as CPU’s and memory, to perform different types of computations.

• Intel Movidius (VPU) is a brand of AI accelerators produced by Intel. These accelerators are designed to accelerate the processing of AI applications and are often used in applications such as image and video recognition, object detection and natural language processing. Intel Movidius accelerators are available in a variety of form factors, including USB sticks and embedded modules – and are often used in edge computing applications.
• Google Edge Tensor Processing Unit (TPU) is a specialised processor that is designed specifically for machine learning and AI applications. It’s used to accelerate the training and inference of deep learning models and is optimised for the matrix calculations that are commonly used in these types of tasks. TPU’s are used in Google’s cloud computing platform and are available to developers through the Google Cloud AI Platform.
• NVIDIA Jetson (CPU+GPU) is a brand of AI accelerators produced by NVIDIA. These accelerators are designed to accelerate the processing of AI applications and are often used in applications such as autonomous vehicles, robotics and surveillance systems. NVIDIA Jetson accelerators are available in a variety of form factors, including embedded modules and developer kits and are often used in edge computing applications.
• Hailo-8 is a VPU (Visual Processing Unit) developed by Hailo, a Israeli-based startup company, that is designed to accelerate AI workloads on edge devices. The Hailo-8 VPU is a small, low-power chip that can be used to perform neural network inference on a variety of edge devices, such as cameras, drones, and robots. It designed for high efficiency and low power consumption to run deep learning neural networks for edge devices.

All of these AI accelerators are used to improve the performance of AI systems by allowing them to process and analyse data faster. They are often used in conjunction with other types of hardware, such as CPUs and memory, to perform different types of computations.

Artificial Intelligence Hardware: Cloud AI

Cloud AI is a type of artificial intelligence (AI) that’s delivered and accessed through the cloud, rather than being run on local hardware. In a cloud AI system, data is stored and processed on remote servers in the cloud, rather than on the user’s local computer or device. This allows users to access and use AI applications and services without having to install software or purchase specialised hardware.

Cloud AI can be accessed through a variety of channels, such as web applications, mobile apps or API (Application Programming Interface) calls. Users can access cloud AI services on a pay-per-use basis, or they can subscribe to a cloud AI service on a monthly or annual basis.

Cloud AI has several advantages over traditional AI systems. It can provide users with access to powerful AI resources and technologies that they might not be able to afford or maintain on their own. It also allows users to scale their AI applications and services up or down as needed, without having to purchase additional hardware.

Cloud AI is used in a variety of applications, including machine learning, natural language processing and image and speech recognition. It’s often used by businesses, researchers, and developers to build and deploy AI applications and services.

Artificial Intelligence Hardware: Edge AI Computing

NVIDIA Jetson, Intel Movidius and Google Edge TPU are all suitable for Edge AI Computing. Edge computing or Edge AI, refers to the use of artificial intelligence technologies at the edge of a network, rather than in the cloud or a data centre.

In traditional AI systems, data is often sent to the cloud or a data centre for processing, which can involve significant latency. Edge AI systems, on the other hand, perform data processing and AI tasks locally, at the edge of the network. This can reduce latencies and improve the responsiveness of the system as data does not have to be transmitted over the network to a remote location for processing.

Edge AI is often used in applications where low latencies are important, such as in the Internet of Things (IoT) and real-time decision-making systems. It’s also useful in situations where there’s limited or unreliable connectivity, as it allows the system to continue functioning even when the connection to the cloud is lost.

Edge AI technologies include small, low-power AI processors and chips that are designed to perform AI tasks locally, as well as software tools and frameworks for developing and deploying AI applications at the edge.

AI Ready Hardware

Like most applications that BVM meet, where AI based projects are concerned – each application demand will differ from the next. So we can meet these differing needs, and through the support of our OEM partner channel, BVM have built (and continue to expand) a portfolio of product to scale for computing efficiencies required around both Edge based PC’s – and Edge servers to provide for reliable performance solutions. All our solutions are provided on industrial lifetime availability programs  – so on a minimum of 3-5 years

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