AI Accelerator: The tool to revolutionize business

As demand for AI continues to grow in various industries, the evolution and widespread adoption of AI accelerators underscore their crucial role in shaping the future of intelligent computing.

These represent a fundamental breakthrough in artificial intelligence, as they significantly improve the performance and efficiency of workloads. Below, we discuss their features, benefits, and examples.

What is an AI Accelerator?

An AI accelerator is a hardware designed to improve the performance and efficiency of artificial intelligence, deep learning, and machine learning algorithms and applications.

They are capable of performing intensive computations faster and more efficiently than conventional processors and can come in different forms, such as graphics processing cards (GPUs), tensor processing units (TPUs), application-specific integrated circuits (ASICs), or programmable FPGA systems.

The main function of these accelerators is to speed up the processing of ML models and algorithms, enabling faster and more efficient execution. This makes them especially useful in applications where real-time response time is critical, such as speech recognition, image processing, machine translation, autonomous driving, etc.

These accelerators are often used in combination with larger computing systems or infrastructures, such as servers or data centers, to boost performance in AI tasks. In fact, it can also offer specific programming interfaces and tools to facilitate the development and implementation of AI algorithms.

Types of AI Accelerators

Accelerators are divided into two architectures according to their function: for data centers and edge computing environments. The former requires a highly scalable architecture and large chips, while the latter focuses more on energy efficiency and the ability to deliver near real-time results.

Integrators at scale

Scale-up integration, or WSI, is a process for building extremely large AI chip networks on a single “super” chip to reduce costs and accelerate the performance of deep learning models.

Nuclear units, or NPUs

NPUs are AI accelerators for Deep Learning and neural networks, and the unique data processing requirements of these workloads.

They can process large amounts of data faster than other chips, as well as perform a wide range of AI tasks associated with ML, such as image recognition and the neural networks behind popular applications like ChatGPT.

GPUS

GPUs are used in a wide variety of devices, including video cards, motherboards, and cell phones.

However, due to their parallel processing capabilities, they are also increasingly used in training AI models.

Field Programmable Gate Arrays

FPGAs are highly customizable accelerators that rely on specialized expertise to be reprogrammed for a specific purpose.

Unlike other accelerators, they have a unique design that is tailored to a specific function, often having to do with real-time data processing. In addition, they are reprogrammable at the hardware level, which allows for a higher level of customization.

Application-specific integrated circuits

ASIS are designed with a specific purpose or workload in mind, such as deep learning.

Unlike FPGAs, they cannot be reprogrammed, but because they are built with a singular purpose, they tend to perform better than other more general-purpose accelerators.

Why are AI Accelerators important?

As the industry expands into new applications and fields, AI accelerators are critical to speed up the data processing needed to create AI applications at scale.

Without AI accelerators, field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs) to accelerate deep learning, advances in AI would take much longer and be more expensive. Therefore, AI accelerators are superior to their older counterparts in three key aspects: speed, efficiency, and design.

Some of their key features include:

• Performance optimization: accelerators are designed to perform intensive computations efficiently and quickly, so they execute AI algorithms more efficiently than conventional processors.
• Parallelism and massive computational capacity: they can perform operations in parallel, allowing them to process large volumes of data and perform complex computations faster, thus accelerating AI model training and inference.
• Specialization in AI tasks: they are optimized to perform tasks such as image processing, speech recognition, and natural language processing.
• Energy efficiency: they can perform computations more efficiently in terms of power consumption compared to conventional processors.
• Specific interfaces and tools: they usually provide interfaces and tools that facilitate the development and implementation of AI algorithms on accelerator hardware.

Challenges faced by AI Accelerators

As we have explained throughout the article, AI accelerators are a fundamental pillar when it comes to developing and scaling applications using this technology, but, the industry also faces some challenges:

• Most are manufactured exclusively in Taiwan: 60% of the world’s semiconductors and 90% of the world’s advanced chips are made in Taiwan and, the largest AI hardware company in the world, Nvidia, is almost exclusively dependent on one company.
• Some AI models are developing faster than the design of accelerators: today’s more powerful models require more computational power than previous ones, and the pace of innovation in chip design often does not keep pace with the innovation occurring in AI models. This is why companies are exploring areas such as in-memory computing and improved performance and fabrication with AI algorithms to increase efficiency.
• They need more power: accelerators are small, making it difficult to direct the amount of energy needed to power them. Advances in the power delivery architectures (PDNs) behind AI accelerators are therefore needed.

AI Accelerators: Use Cases

AI accelerators play a crucial role in the development of new applications, be it robotics, satellites, smartphones, computers, etc. Some of the examples of how they are used are:

Autonomous vehicles

Accelerators can capture data in near real-time, which makes them fundamental in the development of autonomous cars or drones.

Their parallel processing capabilities are unique, allowing them to process and interpret data from cameras, sensors and process it so that vehicles can react to their environment.

For example, when an autonomous vehicle arrives at a traffic light, the accelerators speed up the processing of sensor data, allowing them to read the traffic signal and the position of other vehicles.

LLMs

Large language models rely on accelerators to help them develop their ability to understand and generate natural language.

Parallel processing helps speed up processes in neural networks, optimizing the performance of cutting-edge AI applications such as generative AI and chatbots.

Robotics

Accelerators are critical to the development of the robotics industry due to their ML and machine vision capabilities.

As AI-enhanced robots are developed, accelerators will continue to play a crucial role in developing their capabilities to sense environments and react to them with the same speed and accuracy as human beings.

Edge computing and AI

AI accelerators enable ML tasks to be executed at the edge, rather than moving data to a data center for processing. This reduces latency and energy efficiency in many AI applications.

Example of AI Accelerator: Viawind

Many use cases can benefit from an AI accelerator, and one of them is Viawind, a 3D simulation that we have created at Plain Concepts from real photographs and supported by mathematical calculations. The viewer, based on our Evergine graphics engine, anticipates the 3D landscape study, creating ultra-realistic Asset Digital Twin simulations of wind turbines. These digital twins are used to play with the variables and see the visual impact in an online format, which facilitates real-world decision-making.

We take into account all the variables, including the rotations of the windmills according to the best wind perspective, the rotation of the earth, or the key points of the coast so that the visualization is realistic and objective.

It is therefore a 3D simulation that shows the most reliable, realistic, and scientifically rigorous view of what the project will look like and how it will adapt to the environment. In fact, it solves the major problems faced by energy companies when participating in a bidding process:

• Eliminates inaccuracies and approximations.
• Accurately inform the public and stakeholders of potential impacts.
• Reduces process time.
• Quickly detects negative impacts to correct them.
• Ensures compliance with local legislation.
• Improves approval rate and approval times.
• And many more.

The intelligent combination of 3D reconstructions using Evergine combined with AI models allows us to accelerate and refine the development of visual impact simulations for a wind farm. In particular, the use of AI models is fundamental in two key areas:

• Segmentation of panoramic images to detect ocean, sky, and elements such as coastline and mountains. This makes it possible to automate the generation of elements that occlude the generators, such as the sea horizon, mountains, and foreground objects (street lamps, buildings, etc.)
• Night simulation that transforms a panoramic photograph taken during the day to show how it would look at night. In addition to applying an appropriate color range, it incorporates nighttime elements, such as a starry sky, to enhance visual fidelity. 

The key points of improvement in terms of automation and fidelity in the simulation are:

• The advanced use of geographic information and image tagging allows us to automatically and immediately position the position and dimension of all generators in any panoramic image from a few data points.
• Using Evergine and indicating a few parameters of the model of the wind turbines, we can generate the appearance of these elements perfectly realistic, including the illumination of the day and time that the image was taken, as well as the orientation of the turbines according to the wind. These elements traditionally require specialized 3D design and rendering equipment. With our technology and using Evergine, this task is accomplished in a matter of seconds.
• The use of AI to process and segment the image allows us to understand the scene being simulated, allowing us to better integrate the wind turbines into the scene (hiding behind image elements such as mountains, infrastructures, or the sea horizon itself). This task is traditionally done manually by graphic artists. In Viawind it is done automatically.
• Applying AI to emulate night images avoids the need for a photographer to take night images at the actual location.

An AI Accelerator tailored to you

At Plain Concepts we have the AI Center of Excellence Accelerator, a unique program that allows you to implement an improvement center to lay a solid foundation and bring out the full potential of AI in your organization. You’ll be able to identify, customize, and strengthen workflows, communications, and patterns to deliver high value at high speed.

We help you create a centralized place where best practices based on knowledge and experience are formulated. We accompany you on this journey, ensuring your governance, standardization, adoption, and operational needs, exposing the power of AI through a seamless experience for business users. In addition, we define policies and models that facilitate corporate-wide adoption of standards and consistent implementation.

Benefits you will find:

• Unlock the potential of AI in your organization.
• Accelerate your AI journey with cross-functional experts.
• Get a tailored plan to accelerate AI value delivery.
• Build patterns and teams to adopt high-ROI use cases.
• Improve understanding of how to leverage AI.
• AI solutions to support data migration and modernization.
• Customized roadmap to deliver high-value AI repeatedly.

At Plain Concepts we help you bridge the gap between executive decision-making and AI implementation through:

• Ideation and foresight: explore the power of an AI CoE and align your capabilities with your business opportunities, defining your AI vision and strategy. We guide you to shape one to three use cases for PoC implementation.
• Rapid PoC: We deliver and refine our direction with your business, IT, and leadership stakeholders. You will receive code and artifacts, as well as implemented demo-ready AI CoE solutions.
• Visualization and reading: we lead executive and technical reading to facilitate understanding and knowledge transfer covering demonstrations, implementation, evaluations, and opportunities. You will be equipped to demonstrate value to stakeholders and identify the next steps for your journey to AI excellence.

All in all, with this accelerator you will gain leadership, best practices, research, support, and training on AI and related topics. As an added bonus, it will be the first step for you to develop new and emerging processes, as well as have in your hands a tool to discover, monitor, and disseminate improved opportunities. What are you waiting for to start your journey?