(Source: Leo Rohmann/stock.adobe.com; generated with AI)
In many applications, artificial intelligence (AI) processing at the edge offers the dual benefits of low latency for real-time tasks, such as industrial inspection, and enhanced security where data may be sensitive, such as in medical imaging. Central processing units (CPUs) and graphics processing units (GPUs) can handle many AI tasks, but edge devices have tight power, space, and cost budgets and need deterministic results. The inconsistent timing of CPUs and GPUs can cause issues in applications that require guaranteed, real-time responses.
Field-programmable gate arrays (FPGAs), on the other hand, deliver a flexible logic architecture that can be configured to run AI algorithms as logic circuits rather than as software routines.[1] Since FPGAs run AI models as custom logic circuits, they use power more efficiently than CPUs or GPUs, making them a strong choice for deploying trained AI models at the edge. In this blog, we look further into the advantages FPGAs offer and examine design solutions for these integrated circuits.
When quick decisions are needed, AI processing speed becomes crucial. Because FPGAs can be configured with logic pathways tailored to specific workloads, they provide the repeatable and predictable processing latency needed for high-speed and real-time applications.[2]
Another advantage of FPGAs in edge AI systems is their input-output (I/O) flexibility. Their reconfigurable logic supports both high-speed data interfacing from a CPU and direct sensor-to-FPGA connections. In systems with multiple sensors and cameras, this can significantly offload CPU resources while also reducing latency during FPGA-based AI inferencing.
FPGAs are best suited for fast-moving applications where requirements are changing too quickly for an application-specific integrated circuit (ASIC) to make sense or in applications where the volumes don’t commercially justify ASIC development. The flexibility of the FPGAs also allows for the AI models to be updated as needs and advancements allow. In these instances, their reconfigurability helps engineers who need to tweak performance as the project evolves.
A common approach when using FPGAs for edge AI is to employ them as accelerators for host CPUs. In this architecture, the host processor offloads specialized AI tasks to the FPGA, which executes them more efficiently to enhance overall system performance. Alternatively, FPGAs can serve as standalone processors if their built-in CPU resources are sufficient.
One of the biggest challenges when running AI workloads on FPGAs is balancing model size and accuracy with hardware constraints such as memory and computing power. If a model is too small, it can miss important features, but if it's too large, it won't run efficiently on the device. To address this, developers use optimization techniques that reduce model complexity while maintaining acceptable performance. Some of these techniques include:
After optimization, the model is transferred onto the FPGA's logic units using dedicated software. The resulting implementation can then be tested to confirm it meets accuracy and performance targets.
A common question that comes up is “How do I add AI to my FPGA?” Most FPGA developers have limited familiarity with working with AI, and the same is generally true for many AI developers and FPGA knowledge. A simplified way to integrate AI onto FPGAs is to develop the AI portion as an Intellectual Property (IP) block that can be instantiated in the FPGA in the same way as any other IP block, a natural way for FPGA developers to combine larger systems within the FPGA. This allows both developers to be experts in their own roles but easily work together to integrate AI compute on FPGAs.
The FPGA AI Suite from Altera helps solve the problem of converting the AI model to IP. It allows AI developers to work with models using their existing frameworks like PyTorch and meet target performance with minimal code changes. The model is optimized through OpenVINO and FPGA AI Suite which generates the IP. By connecting the IP and a host processing system, the FPGA team can easily integrate the inference IP and runtime together and deploy AI inference on Altera FPGAs faster.
For engineers looking to integrate a compact FPGA in edge AI applications, the Agilex™ 3 family from Altera provides a high-performance, cost-optimized solution. Compared to earlier cost-optimized Altera devices, Agilex 3 FPGAs offer up to 38 percent lower power consumption along with several enhancements that support AI workloads at the edge:
Figure 1: Block diagram of the Agilex 3 FPGA, illustrating its interface options. (Source: Altera)
With a built-in security module, the Agilex series is well suited for developers targeting data-sensitive edge AI applications such as industrial surveillance, consumer electronics, and medical imaging.
For engineers evaluating the AI capabilities of Agilex 3 FPGAs, the C-Series Development Kit (Figure 2) supports rapid prototyping and application development, with an optional daughter card that adds PCIe connectivity for expanded interface support.
The kit offers a variety of I/O options and hardware resources for rapid prototyping, including:
Figure 2: Agilex 3 FPGA C-Series Development Kit offers a variety of I/O options to support rapid prototyping and application development of trained AI models at the edge. (Source: Altera)
Edge devices are often limited by power and performance, but FPGAs tackle this challenge by running AI models as hardware logic instead of software. By doing this, they deliver deterministic and efficient performance that CPUs and GPUs cannot always match.
Altera has incorporated FPGA fabric infused with AI tensor blocks and advanced built-in features into the Agilex 3 family so the platform can handle AI tasks independently, without depending on a host processor. In addition, FPGA AI Suite from Altera greatly simplifies implementing AI algorithms on FPGAs. As AI finds its way into every part of technology, these kinds of improvements make FPGAs an increasingly important piece of edge computing.
Brandon Lewis has been a deep tech journalist, storyteller, and technical writer for more than a decade, covering software startups, semiconductor giants, and everything in between. His focus areas include embedded processors, hardware, software, and tools as they relate to electronic system integration, IoT/industry 4.0 deployments, and edge AI use cases. He is also an accomplished podcaster, YouTuber, event moderator, and conference presenter, and has held roles as editor-in-chief and technology editor at various electronics engineering trade publications.
[1] https://qbaylogic.com/fpga/ [2] https://www.velvetech.com/blog/fpga-in-high-frequency-trading/ [3] https://www.kaggle.com/code/residentmario/notes-on-weight-sharing [4] https://datature.io/blog/a-comprehensive-guide-to-neural-network-model-pruning [5] https://huggingface.co/docs/optimum/en/concept_guides/quantization [6] https://www.mouser.com/pdfDocs/agilex-3-fpgas-socs-product-brief.pdf
Altera is a leading supplier of programmable hardware, software, and development tools that empower designers of electronic systems to innovate, differentiate, and succeed in their markets. With a broad portfolio of industry-leading FPGAs, SoCs, and design solutions, Altera enables customers to achieve faster time-to-market and unmatched performance in applications spanning data centers, communications, industrial, automotive, and more.