Understanding the Application of Artificial Intelligence in Hardware
The world is moving towards astonishing artificial intelligence solutions for a better future. Scientists are developing new ways to implement AI in different sectors. AI has shown a massive potential to improve the performance and efficiency of our daily devices.
For example, artificial intelligence made it possible to treat patients quickly. It can also predict potential health problems based on age, genetic history, etc. We have already seen an improvement in the computer hardware we utilize daily.
AI hardware is another domain that offers many possibilities for improved performance and reliability. The best example is GPUs or Graphical Processing Units, which are modified to handle tasks.
Due to the extensive parallelism in an average GPU, it can perform various tasks simultaneously. This proves to be an efficient way to train ML models.
As one major part of the AI technology stack, AI hardware is a determining factor in communicating and setting up computations. Let’s learn more about this exciting development of artificial intelligence-optimized hardware.
What exactly is AI Hardware?
Artificial intelligence systems have been prevalent in the market for a long time. But the traditional CPUs limitations did not allow their applications and usability in many cases.
As an alternative to this, AI-optimized hardware came to be in use. New processors such as Graphics Processing Units (GPUs) and Google's TPUs give AI or machine learning algorithms the boost to faster and more efficient computing.
The computational capacity of AI hardware is enough to handle frequent tasks. For example, tricky machine learning algorithms can be handled easily using artificial intelligence hardware instead of a CPU. In addition, AI hardware provides enhanced processing capabilities and can work with huge data sets.
Why do we need AI hardware?
AI hardware applications have seen a significant push in the last few years because of the growth of deep learning and other technologies. Various advanced artificial intelligence applications such as flawless speech recognition, video, text, and image recognition have come into existence these past few years.
Businesses are also increasing their investments in premium artificial intelligence hardware. As a result, chipmakers are frequently building better AI hardware.
Scientists also developed special AI chips (or circuits) with the capability to work faster and more accurately than the human mind for hardware processing needs.
How does it work?
The artificial intelligence hardware contains the following components:
Central Processing Unit- CPUs compute parallel processing through assigned hardware. Reliable multicore CPUs offer computations in modern-day machines.
Graphics Processing Unit- GPUs were originally modelled for multi-dimensional usage like image processing. But its usability has multiplied to several AI processes. The reason is the similarity in image processing and neural computing networks.
Field Programmable Gate Arrays- For inferencing-implementing AI algorithms to actual-world scenarios, we apply the FPGAs.
Application-Specific Integrated Circuits (ASICs)- ASICs are single-purpose integrated circuit chips. Rather than a general purpose, the chip runs a particular usage, such as a video recorder.
Since its development in 2009, there has been steady growth in distributed deep learning. AI experts are keen on improving their artificial intelligence capabilities. They want to develop better algorithms using different computing languages. With AI hardware, these techies can attain every possibility concerning AI tech.
The two most used processors in AI hardware are:-
Graphics Processing Unit is a special chip for rapid processing, specifically designed for computer graphics and image processing. The NVIDIA Jetson device series provides advanced AI power. It can achieve this efficiently by saving power.
Neural networks can operate through NVIDIA Jetpack SDK. Nano-optimized Keras and TensorFlow libraries are also present alongside a 128-core GPU and Quad-core ARM CPU. This allows most of the neural network backends and frameworks to run seamlessly using a little setup.
Intel joined the market by announcing the XE GPUs ("Xe") with multiple graphics processing units in 2020. Even with low power, the Intel GPU XE family achieved state-of-the-art execution.
Tensor Processing Unit (TPU) is a customized AI hardware. It can execute every necessary computation and implement machine learning algorithms. It typically operates on predictive models like Artificial Neural Networks (ANN).
The Google coral edge tensor processing unit is Google's purpose-built ASIC designed to run the AI at the Edge. It is a toolkit building for Edge-AI, enabling production with local AI.
Users can use the Google coral tensor processing unit to form various on-device AI applications. The core advantages are significantly less consumption of power, offline capabilities, and cost efficiency.
Google Coral Edge TPU devices run machine learning frameworks (like YOLO, Tensorflow lite, R-CNN, etc). They can also spot and track objects in videos using connected cameras.
Advantages of Artificial Intelligence Hardware
Since it utilizes GPUs, known to be at most 200 times faster than a CPU, speed is a big differentiator in many cases. As a result, AI works more efficiently using better processors. This means the hardware can compute AI algorithms in less time with more or less the same accuracy.
TPUs and GPUs conduct less power. Even though they require more electricity, their operational speed can save energy by computing at a much faster rate. This is a great advantage to the companies and one step towards greener technology.
GPU boards are being linked with high-speed memory. This increases the computation of AI algorithms. Artificial intelligence models that have difficulty in processing can save time on loading and executing using GPU boards.
Use cases of Artificial Intelligence in Hardware
Specialized circuits are being used affordably in mobile phones. Devices with less power or battery can rely on these compact AI chips for tasks requiring fast AI hardware, such as speech recognition. This reduces power consumption.
Using analog circuits, analysts are researching modifying AI computation. Within a much less space, they can perform computing more complex tasks quickly and rapidly.
Do we need Cutting-Edge AI Chips?
To be cost-effective and more productive simultaneously is a big difference maker for the companies. AI chips are more than capable of handling faster computation. In addition, their unique features make them about a hundred times quicker and more efficient to train.
The traditional AI chips had large, slow, and more power-hungry transistors, resulting in vast energy consumption. Using these old AI chips would mean a rapid increase in the cost and delays in the process.
Due to this continuous growth, the artificial intelligence chip market will reach $263.6 billion by 2031.
Some observable use cases of AI chips are in applications that utilize machine learning algorithms, neural network algorithms, deep neural networks, and NLP (Natural Language Processing).
What does this development mean for the future of such technologies?
Using AI chips for inference helps remove any network lag or instability problems. They're also helpful in protecting data and resolving privacy issues, as they're used as on-device chips. Unlike cloud servers where information is not secure, your data is protected from potential cyber threats.
However, AI chips are only at the initial stage of their development. With more AI hardware and expert systems advancements, we are moving towards powerful chips to handle more complex tasks and reduce potential threats.
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