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What is AI (Artificial Intelligence) Accelerator?
To define it in a few simple words, it can be said that an AI accelerator is a type of a dedicated processor, albeit small, that is specifically designed to speed up machine learning calculations.
An AI accelerator refers to a parallel computation machine. It is specially designed and high performing and can handle processing of AI workloads such as neural networks more efficiently.
- The AI accelerators are specific processors or machine learning hardware chips. These are powerful to run Artificial Intelligence and machine learning applications.
- These accelerators are small but are fast. Created especially to expedite machine learning calculations, it helps data scientists a lot since they need to handle large amounts of data fast.
- Energy efficiency and low cost operation are two most significant advantages of the AI accelerators that use sophisticated algorithms to work without needing any human interference.
- These accelerators follow a strict AI model while evaluating multiple data or while performing any specific task that cannot be executed by a standard CPU.
Understanding AI (Artificial Intelligence) Accelerator
The AI accelerators are powerful and specially designed machine learning hardware chips that can run Artificial Intelligence as well as machine learning applications swiftly and more smoothly.
Generally speaking, the primary objective of the AI accelerator, irrespective of its type, is to evaluate data at a much rapid pace by using specific algorithms but by consuming minimal power in the process.
This means that the AI accelerators are developed especially to follow an algorithmic approach which matches the particular task in hand and helps significantly in solving the problems out-and-out.
Most importantly, it is the computation architecture and the location of the AI Accelerators that are the key to its working process and its functionality.
The AI accelerators today have become an integral part of the modern computing system and therefore it is elementary for all users to know a bit, if not more, about these tools.
As you may know, machine learning, and its subset deep learning in particular, mainly consists of a huge number of linear algebraic calculations. The operations include:
- Matrix to matrix operations
- Matrix to vector operations and more.
The good thing about these particular operations is that all of them can be parallelized easily.
The specialized design of the hardware AI accelerators helps in a lot of ways in such situations. For example:
- It helps in expediting these fundamental machine learning calculations
- It helps in enhancing the performance level of the system overall
- It helps in lowering latency which in turn adds to its performance and speed and
- It helps in reducing the cost of implementing applications that are typically based on machine learning and artificial intelligence.
The AI accelerators usually come with a novel design but with extreme power.
This is achieved typically due to the focus on several vital aspects of their design that expedites the working process eventually.
These aspects are:
- Novel dataflow architectures
- Low precision arithmetic and
- In-memory computing capability.
Typically, an AI integrated circuit chip may contain several billions of MOSFETs or Metal Oxide Semiconductor Field Effect Transistors.
With all these design improvements and useful features, the AI accelerators help in working with specific and complex applications as well as several sensor driven and data intensive tasks which include and are not limited to:
- Artificial Neural Networks
- Machine Learning
There are quite a few significant and specific points at which the AI Accelerators are usually placed.
Before you dive deeper into it, you must know about that. As of now, there are two different AI accelerator spaces namely:
- The data center and
- The edge.
The data centers, especially the hyper-scale data centers, need extremely scalable computer architectures to do their job.
Ideally, it is for the data centers in particular that the chip makers are going big.
For example, Cerebras has initiated the Wafer Scale Engine or WSE which is considered to be the largest chip that has been built ever that can be used in deep learning systems.
On the other hand, the edge signifies the other end of the range. Here, the real estate is pretty limited but energy efficiency is the key.
This is because the intelligence is dispersed at the edge of the network instead of being more centrally located.
It is the integrated AI accelerator IP into edge System on a Chip or SoC device that delivers the results near-instantaneously irrespective of how small it is.
It is very useful for all those interactive programs that are typically run on the smartphones or are used in the field of industrial robotics.
However, the need for using AI accelerators at the edge or in the cloud depends on the particular situation and requirements and therefore can vary.
The Need for AI Accelerators
Usually, when it comes to software designing, the computer scientists normally focus on developing specific algorithmic approaches that match with particular problems.
When they develop such software, they implement it in a procedural language of very high level.
In order to do so, the computer scientists make use of the available hardware in the best possible way in order to make sure that some of the algorithms are threaded.
However, even then, it is very hard for them, if not impossible, to achieve substantial parallelism due to the connotations of the Amdahl’s Law.
However, they now have a new pattern to follow in the form of design by optimization methodology which is the result of big data utilization and the ability to connect almost everything.
According to the design by optimization tactic, the computer and data scientists now can use the innate neural networks and other parallelized computing systems.
This helps them immensely to ingest huge amounts of data in quick time and train them via iterative optimization.
The major workhorses of the industry who are responsible for implementing software find that the standardized ISA or Instruction Set Architecture is not suited for such an approach.
This is why they need the AI accelerators which have emerged so rapidly to deliver the desired results in quick time with their much improved processing power.
The energy efficiency of these tools enables the computer and data scientists to compute large amounts of data at a low cost.
In the last decade, the computing world witnessed a growing prominence of Artificial Intelligence and deep learning workloads.
This, in turn, resulted in an increased demand for hardware units that are specially designed for performing these kinds of complex jobs easily and efficiently.
The computer scientists specially designed these hardware units or computer system components that are made to order from the current products.
This helped in accelerating these tasks with the help of high-output and parallel systems for workstations that are typically targeted at different applications including neural network simulations.
The performance of different hardware AI accelerators depends on their respective architectures, and since the architectures are different, the performances vary as well.
However, to make system level performance possible all these accelerators need a related software stack.
If that is not available then there is a high chance that the hardware accelerator would be underutilized.
Also, the architecture of the accelerators needs to be perfect and corroborate with the high level frameworks of the software in order to facilitate connectivity. Some of these frameworks are TensorFlow and PyTorch.
In addition to that, there are different AI accelerators and machine learning compilers that allow interoperability, such as the Facebook Glow compiler, due to the improved architecture.
Measuring the performance of the AI accelerators is and has always been a debatable topic.
This is due to the greater differentiation that is created in the hardware AI accelerators because the intelligence itself is moving to the edge in several applications.
Talking about the edge, it offers a remarkable diversity of applications that typically need AI accelerators for optimization of a few specific characteristics such as:
- The latency
- The memory and
- Energy efficiency.
All these are based on the varied needs of the end application.
For example, autonomous navigation needs a latency limit of up to 20μs in terms of computational response.
On the other hand, the video and voice assistants need to understand the keywords spoken in less than 10μs and the hand gestures in a couple of hundred milliseconds.
Therefore, these short time windows make measuring the performance of the different AI accelerators quite difficult.
In the future, when human thought processes are simulated, cognitive systems will be more dominant.
This will make measuring their performance a bit easier because the cognitive systems usually have a much deeper understanding of the data and know better ways to interpret them in comparison to the neural networks of today.
This will perhaps make it much faster to measure the performances of the respective systems and the accelerators in them more accurately even at different levels of abstraction.
Benefits of AI Accelerators
There are several benefits offered by these hardware AI accelerators.
One of the most significant benefits is the striking improvements in speed or operation.
The main reason behind it is that the AI accelerators take much less amount of time to train and execute any particular AI model.
Another significant benefit is that the AI accelerators can be used to carry out specialized tasks based on Artificial intelligence which, typically, could not be performed by a regular CPU.
Apart from that, here are a few other top benefits of the AI accelerators with a brief detail against each:
As compared to any other regular computing machine, the AI accelerator can be more energy efficient that can range anywhere between 100 and 1000 times.
The AI accelerators will never draw a huge amount of power, and in turn dissipate a lot of heat while working on a magnanimous amount of calculations.
They simply cannot afford to do that because if these are used in the data center settings it will be needed to keep them cool.
And, if these are used in an edge application the power budget will be significantly low.
Computational Speed and Latency
Well, in terms of computational speed, as said earlier, it is quite high, thanks to the architecture.
However, due to this high speed, the latency is also automatically pushed to the lower side significantly.
This means that the AI accelerators will take very little time to come up with the desired result.
This is extremely important in the safety-critical environments where every second matters such as in the ADAS or Advanced Driver Assistance Systems.
It is very challenging to write a specific algorithm for processing a particular problem, and there are no two ways about it.
However, it is even more challenging and difficult to take the algorithm created and parallelize it along with the numerous cores for faster and additional processing capability.
However, in the neural network world, the AI accelerators play a significant part in making this possible and easier.
With its better design and improved architecture it is not impossible to achieve a performance level and speed enhancement which is at par with the number of cores occupied.
Once again, due to the improved architecture of the AI accelerators, a particular system can take a heterogeneous approach which will allow it to accommodate several specialized processors that will support definite tasks.
This will offer just the level of computational performance that is required by the AI applications.
It also helps in taking the most advantage and making the best use of different devices.
For example, the capacitive and magnetic properties of different memory, silicon structures, and even light can be used optimally for computation purposes.
Boards Using AI Accelerators
There are different boards that are created specifically for using the AI accelerators.
The rise in demand for such boards is due to the slow yet seamless integration of Artificial Intelligence into Internet of Things on edge.
Therefore, more and more companies are now getting engaged in producing these specialized boards.
For example, NVIDIA is considered to be the leading company that makes GPU accelerators.
Here are a few common examples of boards that come with AI accelerators or are built specially for AI applications:
Coral Dev Board of Google
The Coral Dev Board of Google features an edge TPU or Tensor Processing Unit which is a specific type of AI accelerator that is designed by Google.
This board is also called a Single Board Computer or SBC.
The edge TPU present on this SBC is vested with the task of providing high-performance machine learning inference but consumes low power in the process to keep the cost low.
The Coral Dev Board also supports AutoML Vision Edge and TensorFlow Lite which makes it just the right kind of tool for prototyping those particular IoT applications that need machine learning on edge.
When a successful prototype is developed, it can be scaled up to production level by using the SoM or the Coral System on Module on-board along with the custom PCB in combination.
In addition to the original Coral Dev Board, there is also a Coral Dev Board Mini available which is the successor of the Coral Dev Board but comes at a much lower price and a smaller form factor.
BG24 and MG24 wireless SoCs of Silicon Labs
The BG24 and MG24 families were announced by the Silicon Labs in January 2022.
These are actually 2.4 GHz wireless Systems on a Chip that comes with an AI accelerator built in.
These SoCs also come with a new software tool kit.
These SoCs also facilitate higher performance in the battery-powered, wireless edge devices.
These devices typically come with an exclusive security core called the Secure Vault that makes them apt for IoT applications that are highly data sensitive.
The AI accelerators in these SoCs are specifically designed to handle intricate calculations and produce the results quickly and most accurately.
These are more efficient due to the fact that all of the machine learning calculations typically happen within the local device instead of in the cloud.
This reduces the network latency significantly, if not eliminated completely.
In addition to that, this also saves a lot of power in turn because it saves the CPU from doing such kind of processing.
The software toolkit built in them also helps in enhancing its performance with their support to almost all of those popular and commonly used tool suites such as TensorFlow.
Therefore, the BG24 and MG24 wireless SoCs symbolize a remarkable blend of industry abilities such as:
- Machine learning
- Broad wireless multiprotocol support
- Battery life and
- Security for IoT edge applications.
These SoCs are considered to be new software and hardware platforms that are co-optimized and help in bringing out the best performance from the Artificial and machine learning applications.
MAX 78000 Development Board by Maxim Integrated
This is actually an AI microcontroller. It can enable neural networks to perform at ultra-low power consumption.
The hardware in it is based on the Convolutional Neural Network accelerator.
This allows executing AI interferences for battery-powered applications.
The unique aspect of this board is the CNN engine itself that comes with a dedicated weight storage memory measuring up to 442 KB. This can support 1-bit, 2-bit, 4-bit, and 8-bit weights.
It is based on SRAM or Static Random Access Memory and the CNN memory in conjugation allows the updates on the AI network to happen on the fly.
This is because the CNN architecture itself is very flexible which allows the network to be trained in traditional tool sets such as TensorFlow and PyTorch.
Kria KV260 Vision AI Starter Kit by Xilinx
This particular board is considered to be the developed platform of the K26 System on Module or SOM of Xilinx.
This platform targets the vision AI applications in particular in smart factories and smart cities.
This board is also useful in the medical applications as well as in robotics and further factory applications.
Typically, these SOMs are customized so that it allows quicker integration in edge-based applications.
It is typically based on an FPGA or Field Programmable Gate Array and therefore the users are allowed to deploy custom accelerators for vision and machine learning functions by the programmable logic.
Gluon AI Co-processor by AlphaICs
This particular AI accelerator is optimized to allow vision applications.
It ensures that the output is maximized and the power consumption and latency are at the minimum.
This board also allows easy incorporation of neural networks with the use of the Software Development Kit or SDK built in it.
It is typically aimed at the solution providers and Original Equipment Manufacturers of the vision market sector. This includes:
- Industrial manufacturers
- Retail manufacturers
- Surveillance systems manufacturers
- Industrial Internet of Things or IoT devices manufacturers and
- Edge gateway manufacturers.
The evaluation board offered in addition can be used to develop and prototype AI hardware.
Neural Compute Stick 2 by Intel
This is often referred to as Intel NCS2 and it looks very much like a Universal Serial Bus or a USB pen drive.
However, as a matter of fact, this brings both computer vision and Artificial Intelligence to the devices together in a very easy way.
It is also very easy to configure and set up the software environment of it.
It comes with a dedicated hardware AI accelerator for the deep neural network interfaces.
It is also developed on the Movidius Myriad X Vision Processing Unit.
The design and architecture of this tool allows the designers to use it for their computer applications as well as with edge devices such as the Raspberry Pi 3.
This eases the process of prototyping and enhances applications in smart cameras, robots, and drones.
How Does It Work?
The AI accelerators, just as the name suggests, make use of the advanced algorithms to work.
This helps in driving the human-machine feedback but for that there is no need for any human intervention.
It also makes use of the multiple data available and evaluates them accurately by following the strict AI models.
This not only results in higher computational speed but also produces most accurate results and better insights on the given set of data.
The special design of the AI accelerators and its software also helps it to produce fine AI performance.
With the best software optimizations, improvements are made in the working process and it makes it perform 100 times faster.
On the other hand, it also makes use of the common processes that are typically used by the AI models which are further expedited due to the fact that the hardware AI accelerator is silicon optimized.
Different Types of Hardware AI Accelerators
According to the records, one of the first AI hardware accelerators that were used normally was the Graphics Processing Units or GPU.
These chips were specifically designed to work on the graphics and render 3D images in the games.
Another example of AI accelerator application involves the approach using the Wafer Scale Engine that does not need a large chip.
The WSE has all the features to support AI or Artificial research and offers a radically faster speed in computation.
The chip also offers higher scalability in comparison to the traditional architectures.
It has the power to deliver more memory, computation, and communication bandwidth.
Apart from these there are several other types of hardware AI accelerators that can be differentiated based on their uses and applications such as:
- Vision Processing Unit or VPU
- Field Programmable Gate Array or FPGA
- Application Specific Integrated Circuit or ASIC and
- Tensor Processing Unit or TPU.
All of these are separate chips and can be used separately in smaller systems or in a combination of tens or hundreds in larger systems, as per the requirements. However, the aim is to help in processing big neural networks.
The Coarse Grain Reconfigurable Architecture or CGRA of some offer significant momentum since they have the ability to offer smart tradeoffs between energy efficiency and performance on one side and the suppleness to program diverse networks on the other.
The uses and functionalities of all of the hardware AI accelerators are briefly explained below.
Graphics Processing Unit
One of the most commonly used hardware AI accelerators is the Graphics Processing Unit which is a specialized chip that expedites graphics processing and guarantees smoother and seamless rendering of images.
Rendering images is the primary purpose of the GPU and therefore it has become an integral part of supercomputing these days.
These chips are being used increasingly in the hyper-scale data centers to speed up all sorts of tasks performed here whether it is networking, encryption, or AI.
The GPUs are considered to have ignited an AI boom and has been driving continual advances in professional graphics and gaming, thereby becoming the major part of the modern supercomputers.
The parallel structures of the GPUs help in handling image processing as well as computer graphics with great efficiency to be more productive and valuable in comparison to the Central Processing Units or CPUs that are used for general purposes.
GPUs can process large blocks of data by using better algorithms in parallel. In the supercomputers and workstations multiple GPUs are used to process multiple videos simultaneously in quick time.
These chips are also used for other purposes such as:
- 3D rendering
- AI for training workloads
- VFX and
Some particular types of GPUs, such as those from NVIDIA, usually come with chips that have CUDA Cores.
Each of these cores acts like a tiny processor and can execute a few codes.
Vision Processing Unit
The Vision Processing Units are specific types of microprocessors that are growing in popularity and demand.
This is a specific type of hardware AI accelerator that is designed especially to handle and expedite the machine vision tasks.
The VPUs are known to be more capable in handling different types of machine vision algorithms.
The specific design and resources of these tools can capture visual data from different sources such as cameras and are capable of parallel processing.
A few specific types of GPUs use much low power but produce a high performance.
These can be plugged into any interface that supports programmable use.
These tools do not need to use any off-chip buffer to capture data from a camera and instead use the direct interfaces included.
They give additional emphasis on data flow on the chip and the several parallel execution units.
There are several factors that drive the use of VPUs extensively today which are:
- The growing adoption of edge AI and edge devices
- The increasing adoption of smartphones
- The need to integrate machine vision acceleration into the mobile devices such as smartphones and others and
- The growing requirement for advanced computing competence for computer vision.
The target market of the Vision processing units include:
- The internet of things or IoT devices and
- The new segment of devices for AR/VR.
The VPUs can also perform several different machine vision algorithms which includes Convolutional Neural Networks or CNN and Scale Invariant Feature Transform or SIFT and other similar types of vision algorithms.
Field-Programmable Gate Array
The Field Programmable Gate Array refers to the IC or Integrated Circuit that can be configured after manufacturing by a consumer or a designer.
That is the particular reason for calling it Field Programmable.
The FPGAs include a wide variety of programmable logic blocks. It also comes with a hierarchy of modifiable interconnects.
Both these enable the logic blocks to be connected to each other which form several logic gates.
All these can be inter-wired in a variety of configurations.
The FPGAs are better than the GPUs in the sense that it offers more interface flexibility.
Also, the performance of the FPGAs is further improved by the combination of programmable logic with the standard peripherals and the Central Processing Unit.
The GPUs, on the other hand, use thousands of smaller cores in them that help in optimizing parallel processing of floating-point operations.
The FPGAs come with higher processing capabilities and increased power efficiency that enables them to perform a wide range of logical functions at the same time.
However, these are not suitable for developing technologies such as deep learning applications and self-driving cars.
The FPGAs of today come with useful resources of RAM blocks and logic gates that help in making complex data calculations.
The programmable character of the FPGAs makes them a perfect fit for several different markets.
It is this fact that these can be reprogrammed after manufacturing according to the needs of the functionality and application separates it from the custom made Application Specific Integrated Circuits or ASICs which can perform only a particular design task.
The FPGAs are now more and more applied in the data centers to speed up AI workloads and to work on more of them as well as machine learning inference.
The modern FPGA accelerator cards satisfy the growing demand for heterogeneous architectures in businesses and offer performance improvements when they work on increased AI workloads.
Application-Specific Integrated Circuit
Commonly referred to as the ASICs, these are an entire new category of AI hardware accelerator gaining prominence.
The ASICs typically use specific strategies such as lower precision arithmetic and optimized memory which speeds up the calculation process and increases the output of computation.
Some specific types of ASICs, such as the Nervana of Intel, offer support for a huge amount of parallelization in server settings.
The structure of the chip here is refurbished significantly and is built on the 10 nanometer manufacturing process.
The ASICs typically offer a lot of different advantages and, once again, just like other hardware AI accelerators, speed is one of the main advantages of using them.
Also, just like other accelerators, it minimizes the amount of time taken to train the AI model while executing any specialized AI-based tasks.
Tensor Processing Unit
Finally, the Tensor Processing Unit is a hardware AI accelerator of Google that comes with a specially designed circuit.
This circuit helps it to incorporate all of those essential arithmetic and control logics.
These logics are particularly needed to run machine learning algorithms.
Google launched the TPUs in 2016 and these can run specific algorithms by using different predictive models such as:
- ANN or Artificial Neural Networks
- RFs or Random Forests and more.
Much unlike the GPUs, the TPUs are usually custom-designed.
This enables them to handle complex computing tasks such as matrix multiplications during neural network training.
Typically, the Google TPUs come in two basic types such as:
- The cloud TPU and
- The edge TPU.
The cloud TPUs can be accessed from the Google Colab notebook. This gives TPU pods to the users that are located in the data centers of Google.
On the other hand, the custom-built development kit of the edge TPU can be used to create definite applications.
The tensors are usually multi-dimensional matrices or arrays.
These are the basic units that hold specific data points such as weights of any particular node within the neural network in a particular row and column format.
It is the tensors where the basic operations are done.
Therefore, with all these different and useful types of hardware AI accelerators and the different benefits provided by them there are no doubts that these are growing in popularity and usage with each passing day.
However, the need for all of them or any particular accelerator will depend on the application type and their suitability to handle specific applications. This will also determine their fate in the following years.
Therefore, it is now left to the engineers to decide how best they can design them to accommodate the growing demands for the AI systems in the market.
And, the good news is that there are no stones left unturned by the designers to find a better way to achieve that through continual research and development.
Still, leaving this aspect aside, as of now, more and more users are utilizing the AI accelerators to keep up with the development pace of modern technology and achieve better and faster computing results on real-time data.
That concludes this article based on the different aspects of AI accelerators.
Now you are well aware of the working process, the benefits offered and the different types of AI accelerators and can surely make a decision to choose the best suited one for your computing jobs.