Hardware Accelerators for Machine Learning Explained – GPU for Deep Learning



What is hardware acceleration? Hardware acceleration involves offloading specific tasks from a CPU to specialized hardware for improved performance.

What is a hardware accelerator? A hardware accelerator is a dedicated component optimized for specific types of computational tasks.

What hardware is needed for machine learning? Machine learning typically requires hardware components like GPUs and TPUs for efficient processing.

What is AI accelerator? An AI accelerator is hardware designed to speed up artificial intelligence tasks, often including specialized chips like TPUs.

What are the benefits of hardware acceleration? The benefits of hardware acceleration include increased efficiency, energy savings, and enhanced performance in tasks like machine learning and artificial intelligence.


You open up your laptop, check Surfline, and are greeted by perfect conditions— there is a flawless 4-foot swell with light offshore winds. Inspired by the surf report, you make your way to the garage to grab your surfboard. You are now faced with a decision: take a 20-minute bike ride to your favorite surf spot or use the convenience of your car with the keys ready to go. The choice is yours.

While this analogy may not be perfect, it closely mirrors the decision between utilizing hardware acceleration and forgoing it. Opting for hardware acceleration is selecting the car; you are making the choice to shift the workload required for the task (reaching the beach) away from your legs and onto your vehicle. The car will transport you to the beach much faster than the bike, sparing your quads from the intense burn of the journey.

Modern web applications have become more resource-intensive, causing underpowered computers to experience slowdowns.


Users now have the ability to enable hardware acceleration to shift certain tasks from the CPU to other hardware components like the GPU.

This is particularly beneficial for tasks such as video streaming and gaming, though its availability and usage depend on the specific software or application.

Understanding Hardware Acceleration

The CPU is the most important component of a computer, it performs all the arithmetic and logic for the computer and turns this data into usable output for the machine.

CPUs fall under the category of general-purpose processors as they are made for a wide range of computing tasks. When we are completing a specific task, general-purpose processing will get the job done, but there are better more efficient options.


Hardware acceleration is the process of offloading resource intensive tasks to hardware that is better suited for the job.

For example, computers with an integrated GPU or no GPU at all will struggle to play certain games, while a PC with the same specs, but with the addition of a high-end GPU will play the same game flawlessly.

Types of Hardware Accelerators

GPU: Graphics Processing Units are specialized chips that are highly regarded for their ability to render images and perform complex mathematical calculations. When it comes to machine learning, GPUs are highly effective.


They excel at speeding up the training of deep learning models like Convolutional Neural Networks (CNNs).

This makes them an essential element of AI development. By using multiple GPUs in parallel, supercomputers can process massive datasets quickly and efficiently.

VPU: The development of Vision Processing Units is a significant advancement in the field of machine learning. These units are designed specifically to excel in tasks that involve recognizing objects and classifying images.


Their ability to handle machine learning algorithms like CNNs and SIFT makes them a valuable tool in the growth of artificial intelligence.

The increasing demand for VPUs is driven by the widespread use of smartphones and the need for advanced vision-based features.

FPGA: Field-Programmable Gate Arrays are a popular choice for machine learning tasks because they can be reconfigured after they are manufactured. They provide a flexible and efficient hardware platform that is unique to them.


Their ability to adapt to specific machine learning workloads allows developers to optimize and accelerate algorithms. Also, FPGAs are capable of parallel processing.

TPU: Tensor Processing Units are custom-designed ASICs developed by Google for accelerating machine learning workloads, particularly those involving neural networks.


TPUs are optimized to perform matrix operations at high speeds, making them ideal for training and inference tasks in artificial intelligence applications.

They are well-suited for tasks that require vast amounts of matrix multiplications.

ASIC: Application-Specific Integrated Circuits are custom-designed chips created for specific functions or applications. They are highly specialized and optimized for particular tasks, often offering unmatched performance and energy efficiency.


ASICs are frequently used in situations where standard processors such as CPUs or GPUs would be less effective.

They find applications in various domains, including cryptocurrency mining, networking, and specialized machine-learning tasks.

Why You Should Use Hardware Acceleration

Let’s use machine learning as a means to highlight the importance of hardware acceleration.

The impact of using hardware acceleration on a systems energy efficiency can be remarkably impressive.


GPUs can achieve more than 40 times greater energy efficiency than CPUs when used for training AI models.

ASICs and FPGAs tailored for specific tasks can provide users with even more outstanding energy efficiency results.

GPUs thrive on repetitiveness, making them ideal for processing tasks where the same operation is applied to many data points, such as manipulating individual pixels on a screen. They use something known as data segregation, which means each data point only requires information from its nearby neighbors, reducing the need for global data access.

GPUs are incredibly efficient at handling conditional logic and memory access, thanks to their ability to autonomously make decisions and prioritize nearby memory access. This makes them essential for application ranging from video rendering to deep learning, where fast data manipulation and parallel processing are critical.

When facing hardware-related challenges, consider disabling hardware acceleration as a last resort and opt for thorough troubleshooting to pinpoint the issue’s source.

Remember that your GPU is an expensive piece of hardware specifically designed to excel in computational tasks, making it a resource that should be fully utilized.

How Hardware Acceleration Works

The decision on what to hand off to a hardware accelerator is typically made by software running on the CPU. This software recognizes tasks that can benefit from hardware acceleration based on predefined criteria or user configurations.

When a task is identified for hardware acceleration, the CPU prepares the necessary data and instructions for the hardware accelerator. This involves setting up the appropriate parameters, loading data into memory accessible by the accelerator, and instructing it on how to execute the task. Once the setup is complete, the CPU hands off control to the hardware accelerator.

The hardware accelerator takes over the task, executing it with high efficiency due to its specialized architecture. The CPU is then free to perform other tasks or manage the overall system. The handoff is managed through interfaces and communication protocols.

After the hardware accelerator completes the task, it may return results or signal its completion to the CPU, which can then resume control and continue processing or handling the next task.


Specialized hardware accelerators have transformed the landscape of machine learning and AI. These technological advancements have made training neural networks faster and more efficient.

With GPUs, TPUs, VPUs, FPGAs, and ASICs leading the charge, developers and researchers now have the tools to push the boundaries of AI capabilities.