By definition, an embedded system is a combination of a computer processor, computer memory, and input/output peripheral devices that has a dedicated function within a larger mechanical or electronic system. Today’s embedded systems can incorporate a variety of different processor types depending on the desired function and system requirements. Engineers can select from FGPAs, GPUs, and the commonly-used CPU to allow the system to process information and perform calculations and different tasks.
Today, both CPUs and GPUs are commonly utilized within embedded systems as embedded processors to optimize system performance. In this article, we’ll provide insight into their differences, how are they used together, and the advantages they each hold when processing data.
What is a CPU?
A CPU, or central processing unit, is considered to be the “brain” of a computer and is responsible for fetching and decoding data, performing arithmetic/logic calculations, and processing operations to allow the system to perform a specific task.
CPU; Photo by A CPU is composed of a few heavyweight cores, each with their own ALUs, control units, and cache, along with a high clock speed. It processes data sequentially, meaning it receives and processes data in order, one task at a time. It is, however, latency-optimized, so it is capable of quickly switching between executing tasks.
What is a GPU?
A GPU, or graphics processing unit, operates similarly to a CPU, but is mainly responsible for rendering graphics and images by performing rapid mathematical calculations.
A GPU consists thousands of lightweight cores that are designed for simple integer or floating-point calculations. It operates through a method called parallel processing where it can simultaneously process multiple sets of data across many processing units. Because of this, GPUs are optimized for a high throughput.
CPU vs GPU
Both CPUs and GPUs are similar in that they are both microprocessors built to process data. They both use many of the same components within their architecture, but are built differently, each optimized for a different purpose.
The main difference between a CPU and GPU is the number of cores within each processor and how they process data and instructions given to them. As mentioned earlier, a CPU has just a few cores – around four to eight (generally up to twenty-four) - but they are much larger and more optimized to handle a variety of computations and tasks. It could be thought that these cores are “smarter” than GPU cores, which makes a CPU equipped to performing advanced calculations and even running databases.
GPUs, on the other hand, consist of hundreds to thousands of smaller, more specialized cores. Data sets are divided across multiple cores and are processed all at once in parallel. This allows the processor to perform simple and repetitive calculations at a very quick rate. Because of this, GPUs are most ideal for forming images at higher resolution and faster frame rate and for rendering graphics, where textures, lighting, and rendering of shapes are performed concurrently in order to produce high quality images and video on screen.
| CPU | GPU |
| A smaller number of larger cores (up to 24) | A larger number (thousands) of smaller cores |
| Low latency | High throughput |
| Optimized for serial processing | Optimized for parallel processing |
| Performs fewer instructions per clock | Performs more instructions per clock |
| Automatic cache management | Allows for manual memory management |
| Cost-efficient for smaller workloads | Cost-efficient for bigger workloads |
CPUs and GPUs Used Together
In the early days of computing, the CPU was used to render computer graphics and process images in embedded systems, but with the development of more graphic-intensive applications, this put a strain on the CPU and its performance. To help offset this workload, GPUs were developed to perform specialized, repetitive tasks to render 3D graphics more quickly and efficiently. In today’s more advanced video applications, developers utilize the GPU to provide the smooth graphics that consumers expect in modern videos and video games.
Today, electronics from smartphones to tablets and laptop computers utilize multi-core processors that include both a GPU and CPU to ensure the system operates optimally and delivers on performance, particularly for the rise in artificial intelligence (AI) and virtual reality applications that utilize life-like visuals.
Integrated Graphics vs Graphics Card
When used together within a system, the CPU and GPU can either be included on the same chip, or the GPU can be a completely separate piece of hardware, called the graphics card.
A processor that includes a GPU is considered as integrated graphics hardware. This means that the processor can handle both GPU and CPU processes on the same chip. In this scenario, the GPU is still responsible for rendering images and graphics, not the CPU. When incorporating both the CPU and GPU onto the same chip, this often requires downsizing both of these processors, reducing its number of cores, therefore limiting its processing power.
Conversely, by using a graphics card, the GPU is solely dedicated to graphics processing. This makes them much more powerful than integrated GPUs and frees up memory use for the CPU.
Graphics Card. Image by The Use of CPUs and GPUs in Today’s Technology
In today’s modern technology, the use of both CPUs and GPUs is more pertinent than ever. The adoption of GPU use has accelerated with the rising demand of rendering graphics at high speeds for life-like video gaming and video production, as well as performing non-graphical tasks such as machine learning and scientific computation. Together, these technologies have paved the way for many modern-day systems and applications, including:
- Video gaming/Virtual-reality gaming: Both the CPU and GPU are essential in producing high-quality video games. With today’s games having a higher emphasis on graphics and reliance on user inputs, both processors pull their weight to make this a reality for the user. For instance, the CPU takes inputs from various sources, one being a player’s head mounted display and hand controllers. The GPU handles the visuals aspects, performing calculations to produce lighting, shading, special effects, etc.
- Machine Learning/AI: The use of GPUs has allowed machine learning and artificial intelligence to rapidly take off in the tech industry. Both require large amounts of data to be processed at a time in order to for these systems to “think” swiftly and intelligently. With its parallel-processing capabilities and high throughput advantages, the GPU can handle specialized, complex mathematical calculations at a quick rate to address this need.
- Cryptocurrency mining: Obtaining virtual currencies like Bitcoin involves using computational power to solve complex cryptographic hashes. While the CPU is capable of performing this task, a GPU can better manage large amounts of cryptographic data and can help the computer generate currency much faster.
- Analytics and Data Science: With its parallelism, GPUs are well-suited for analytics programs where they can process large amounts of data from different sources and speed up tasks that involve inspection and searching through vast databases.
Embedded Tools to Develop and Debug Systems with CPUs
With the growing complexity of systems in our modern world, having the right debugging tools can help expedite the testing and development processes. Total Phase offers host adapters and protocol analyzers that allow embedded engineers to quickly and easily develop and debug their I2C, SPI, CAN, USB, and eSPI systems.
Host Adapters
Our I2C or SPI host adapters can emulate master or slave devices to validate responses and communication occurring on the bus. As a master device, our host adapters can evaluate peripherals such as sensors and memory chips, and can also be used to emulate a slave device to test commands sent from microcontroller units or CPUs.
The Aardvark I2C/SPI Host Adapter is a general-purpose host adapter that can emulate an I2C or SPI master or slave device. It can signal up to 800 kHz as an I2C master, up to 8 MHz as an SPI master, and up to 4 MHz as an SPI slave. It allows users to perform multiple applications including prototyping and production testing.
The Cheetah SPI Host Adapter is a fast and powerful USB-to-SPI host adapter, capable of communicating at up to 40+ MHz. It is an ideal tool to develop, debug, and program SPI applications, helping users focus on core competencies by minimizing debugging and programming time.
The Promira Serial Platform is an FPGA-based platform that supports a variety of different protocols, speeds, and functionalities through downloadable applications. It is able to emulate an I2C or SPI master or slave device, and can signal up to 80 MHz as an SPI master, up to 20 MHz as an SPI slave, and up to 3.4 MHz as an I2C master or slave. It offers advanced features and capabilities that make it ideal for a number of different applications, whether it be prototyping or Flash programming memory devices.
Protocol Analyzers
Our I2C/SPI protocol analyzer is a helpful tool to monitor bus data in real time. This allows engineers to capture data on the bus and analyze it to ensure all communication between devices is correct and contains no errors.
The Beagle I2C/SPI Protocol Analyzer can non-intrusively monitor I2C traffic in real time up to 4 MHz and SPI up to 24 MHz.
Conclusion
CPUs and GPUs are both critical for the function of embedded systems. While these two are able to process data and perform calculations, they do so in different ways, each to their own advantage. Today, there is a rise in the use of GPUs within systems as the need for complex, high-volume data processing becomes more and more desirable for modern-day applications. With the use of the right tools, embedded engineers are able to gain visibility into their systems to make this possible.
To learn more about how our tools can assist with developing your own embedded system, please contact us at sales@totalphase.com.