Embedded Systems with FPGA Acceleration
Traditionally, deep learning training and inference analysis were performed on powerful servers in data centers. However, as applications came online requiring real-time data processing and analysis, deep learning inference analysis moved to the edge. The edge addresses some of the problems associated with processing and analyzing data in the cloud.
AI edge computing addresses the latency issues involved with sending data to a data center that’s thousands of miles away, waiting for the cloud to execute an algorithm on the data, and send an output to the origin device. Edge computing addresses the latency issue because data is processed, and AI algorithms are applied locally on the data at the source of data generation, eliminating the need for data to travel thousands of miles to a data center for processing and analysis and back to the device of origin. Premio offers a variety of embedded computing solutions that can be equipped with FPGAs to accelerate AI workloads at the edge.
Performing AI inference analysis at the edge is increasing in importance as the number of IoT (internet of things) and IIoT (industrial IoT) devices continue to increase so does the data generated by such devices. So, using the data generated by such devices to perform AI inference analysis at the edge gives businesses a competitive advantage and drives business value by providing them with rich situational insights and performance enhancement for greater quality control, safety, security, and efficiency.
AI inference analysis at the edge is different from the traditional model, where data was collected and only analyzed using AI at a later time. Computing power and storage technology have improved to the point of allowing organizations to run AI inference at the edge where the data is being captured or generated.
That said, before bringing AI inference analysis to the edge, deep learning training must still be performed in data centers due to the complexity and massive amounts of data that must be fed to DNNs (deep neural networks) to train them. However, once a DNN is trained, the trained model can be deployed onto an edge-computing solution to perform deep learning inference at the edge. The trained model that’s deployed at the edge is then used to perform inference analysis on new data that it has never seen before. If you are looking for an edge AI inference computer, you should explore the wide variety of edge AI computing solutions offered by Premio.
What is an FPGA Accelerator?
Field programmable gate arrays (FPGAs) are integrated circuits where the circuits can be re-programmed and reconfigured as needed for specific workloads. This is the main difference between FPGAs and GPUs. In GPUs, the circuitry is hard etched and cannot be re-programmed. FPGAs offer flexibility, programmability, speed, providing organizations with a solution that does not come with the complexity of developing custom ASICs (application-specific integrated circuits). Embedded systems can be configured with FPGAs providing organizations with systems capable of accelerating their AI workloads at the edge, processing and analyzing data in real-time.
Source: Intel (Intel Arria FPGA Accelerator)
What makes FPGAs desirable?
FPGAs are desirable because of their ability to accelerate AI workloads. This is so because they can be configured and programmed to deliver performance similar to that offered by GPUs and ASICs. Since AI applications change rapidly, the reconfigurable and reprogrammable nature of FPGAs makes them the ideal solution because they are capable of evolving along with the ever-evolving AI landscape. That is, they allow designers to quickly test their new algorithms and bring them to market as quickly as possible.
Furthermore, FPGAs are desirable for AI and deep learning applications because they provide a variety of benefits. Here are some of those benefits:
FPGAs eliminate memory buffering and overcome I/O bottlenecks, which often limit the performance of AI systems. That is, FPGAs accelerate data ingestion, speeding up an entire AI workflow. This is so because the ability to ingest data quickly significantly decreases the amount of latency, which is a requirement for systems that are used to perform mission-critical applications that require real-time analysis and decision making.
Also, FPGAs are excellent for AI workloads where data is gathered from multiple sensors and/or cameras. For example, they are great for autonomous vehicles where data is fed to the system from various sensors, cameras, LiDAR, and audio sensors because of FPGAs’ ability to handle multiple data inputs.
Additionally, some research has shown that FPGAs are extremely power efficient than GPUs for performing AI inference analysis. This is so because the logic offered by FPGAs is extremely efficient at executing applications, which offers higher performance per watt. This is so because FPGAs are capable of performing the same function that a CPU can perform by executing thousands of cycles in just a few cycles.
Ultimately, the biggest benefit of an AI inference computer equipped with an FPGA is the ability of the FPGA to re-programmed and reconfigured for each specific application. The reconfigurability allows organizations to deploy FPGAs inference PCs to perform the latest deep learning inference innovations as they emerge.
Benefits of FPGAs for Accelerating AI Workloads
FPGAs are a great option for accelerating inference analysis performed at the edge because they are extremely fast, extremely flexible, and they are very power efficient, making them great for deployment at the edge.
Some organizations are performing deep learning inference analysis using CPUs to host FPGAs that run the inference application. FPGAs are especially important for mission-critical applications, such as autonomous vehicles and factory automation, because such applications require ultra-low latency analysis and decision making where every millisecond counts.
Furthermore, FPGAs provide highly customizable I/O, which is extremely important for inference analysis because it allows FPGAs to integrate with numerous sensors, cameras, and other devices that provide it with data.
Moreover, FPGAs reduce the total cost of ownership (TCO) because the same FPGA can be used to accommodate a variety of different tasks dy re-programming them through software to undertake new workloads. This is extremely important for AI applications where the landscape is continually changing and evolving, requiring the hardware to evolve with it.
Now that we’ve discussed many of the benefits of using FPGAs, there is only one drawback that we have to mention, and that drawback is programming FPGAs. Programming FPGAs is difficult requires a trained individual who is familiar with programming them. This includes finding an individual who is familiar with hardware programming languages (HDLs), and knows how to use the tools that are often supplied with FPGAs. Once you have this individual, you’re ready to accelerate your AI workloads using FPGAs.
Overall, FPGAs are capable of performing inference analysis in data centers and at the edge thanks to the power, flexibility, ultra-low latency, and energy efficiency that they offer. FPGAs are capable of accelerating workloads that include image classification, object recognition, computer vision, autonomous vehicles, and medical diagnostics. The main feature of FPGA (field-programmable gate arrays) is the ability that it offers to organizations to program and customize the hardware via software. This flexibility is of paramount importance for organizations in the AI field where technology and algorithms are always changing and evolving, offering organization longevity and reliability.
Premio offers a wide variety of industrial computers equipped with FPGAs, enabling systems to accelerate AI workloads at the edge. Premio has been manufacturing embedded computing solutions in the United States for over 30 years. They make premium embedded computers that are capable of surviving challenging deployments in extreme environments. If you need assistance choosing an industrial PC with FPGA acceleration, please contact one of our embedded computing professionals and they will be more than happy to assist you with finding a solution that meets your specific requirements.