What Is an FPGA?

FPGAs are a type of computing component that can be reprogrammed to serve different purposes. Compared to processing technologies such as CPUs and GPUs, FPGAs are more versatile, with the ability to be reconfigured to suit a wide variety of use cases. FPGAs offer a combination of speed, programmability, and flexibility to deliver performance without the cost and complexity of developing custom chips.

In the AI field, FPGAs are often used as AI accelerators and AI processors that help enable AI workloads from edge to cloud. The interconnectivity within an FPGA resembles the neural wiring in the human brain. The programmable logic fabric within an FPGA is similarly connected, which is one reason why FPGAs are excellent implementation targets for neural networks and other AI workloads. FPGAs are deployed in conjunction with the CPU to handle specific functions that are vital to the success of the overall AI application.

Role of FPGAs in AI

FPGAs are used to support AI applications in the cloud, in the data center, and at the edge. They’re effective tools to help eliminate memory buffering and overcome I/O bottlenecks, which are among the most limiting constraints in AI system performance. By accelerating data ingestion, FPGAs can speed up the entire AI workflow.

FPGAs are especially suited for edge AI in various industrial, medical, test and measurement, aerospace, defense, and automotive applications. They can be deployed in both AI servers and embedded devices to power a variety of advanced use cases. Using FPGAs in solution designs provides several advantages that help them support diverse data needs at the edge, including robust support for I/O protocols, low latency, low power, and long deployment lifetimes.

Additionally, FPGAs are used in networking for AI applications. To support AI, networks must facilitate rapid data transfers and communication between edge devices, cloud services, and other interconnected components. To help make it possible, FPGAs are equipped with the latest generation of high-speed I/O standards and can be used to accelerate wireless and wireline networking usage. They can also be used to enable networks to add AI-enhanced capabilities to support emerging applications such as anomaly detection, wireless channel estimation, and wireless decoder convergence.

Meanwhile, cloud and data center environments are seeing FPGAs being applied to accelerate databases, genomics, and networking and help optimize AI inference tasks, such as large language models, conversational AI, and recommendation systems They’re also used for neural network applications, including anomaly detection, financial fraud detection, and high-speed trading. In these high-performance data center environments, the high energy efficiency of FPGAs helps mitigate cooling costs and supports the development of greener AI technologies.

Benefits of FPGAs for AI

FPGAs offer multiple benefits when used for AI:

• Easily and efficiently add new capabilities: Using FPGAs, designers can add AI capabilities, like deep packet inspection or financial fraud detection, to existing workloads. FPGAs also make it possible to add security, I/O, networking, or pre/postprocessing capabilities without requiring an extra chip.
• Achieve great performance with high throughput and low latency: FPGAs can inherently provide low latency and deterministic latency for real-time applications like video streaming, transcription, and action recognition by directly ingesting video into the FPGA, bypassing a CPU. Designers can build a neural network from the ground up and structure the FPGA to suit the model best.
• Improve ROI through inherent flexibility: The circuitry inside an FPGA chip is not hard etched; it can be reprogrammed as needed. This makes them one of the most cost-effective hardware options available. Plus, they can be used for more than just AI. By integrating additional capabilities onto the same chip, designers can save on cost and board space. FPGAs have long product life cycles, so hardware designs based on FPGAs can have a long product life, measured in years or decades. This characteristic makes them ideal for use in industrial, defense, medical, and automotive markets.
• Consume less power: With FPGAs, designers can fine-tune the hardware to the application, helping meet power efficiency requirements. FPGAs can also accommodate multiple functions, delivering more energy efficiency from the chip. It’s possible to use a portion of an FPGA for a function, rather than the entire chip, allowing the FPGA to host multiple functions in parallel.
• Optimize I/O: FPGAs excel when handling data input from multiple sensors, such as cameras, LIDAR, and audio sensors. This ability can be extremely valuable when designing autonomous vehicles, robotics, and industrial equipment.

FPGA Challenges

One of the few hurdles to overcome when using FPGAs is that the hardware typically requires specialized programming expertise. To address this challenge, software-based FPGA programming models are being used to reduce the amount of expertise needed. These higher-level programming models allow a data scientist or model developer to create a neural network using a common AI framework, such as TensorFlow or Caffe, and deploy it on an FPGA without knowing the details of the FPGA architecture.

FPGA AI Solutions

FPGAs for AI are used to enable many use cases across industries:

• Health and life sciences: Applications include medical monitors; 2D diagnostic equipment with image recognition and object detection, such as X-ray equipment and endoscopes; other types of pathology detection; genome sequencing; and surgical robotics.
• Military and aerospace: Uses include unmanned aerial vehicles (UAVs), target detection, and radar detection and classification.
• Industrial applications: FPGAs add AI-based detection and real-time control at the edge.
• Audiovisual applications: Examples are videoconferencing cameras with face detection for automated panning and zooming, and studio cameras with automatic face detection for precise focusing.
• Broadcast video: Uses include standard dynamic range to high dynamic range conversion, intelligent conversion among video resolutions, and variable frame rate video capture and display.
• Consumer applications: An example is 3D displays with eye detection and tracking for stereo imaging.
• Automotive applications: Uses include object detection in autonomous vehicles and infotainment system control and customization.