How FPGAs Enable Efficient Edge AI

Cost-optimized FPGAs Accelerate AI with Configurable Logic

(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.

Why FPGAs Beat CPUs and GPUs at the Edge

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.

Design Considerations for FPGA-Based AI Systems

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:

• Weight sharing: Reduces the number of parameters by letting similar neurons use the same weights, helping the model recognize features, even if they shift position.^[3]
• Model pruning: Eliminates parameters with minimal impact, reducing model size and computational overhead.^[4]
• Quantization: Converts model weights to lower-precision data types (e.g., 32-bit floating point to 8-bit integer), decreasing memory usage and improving processing speed.^[5]

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.

Software Designed to Simplify AI Deployment on FPGAs

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.

FPGAs Designed for Edge AI Applications

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:

• 2^nd Gen. HyperFlex core fabric: Enhanced FPGA fabric delivers faster, more efficient processing through the use of registers distributed throughout the routing fabric. Reduced bus widths help shrink package size, enabling more functionality in compact designs.^[6]
• Power-efficient I/O: Advanced connectivity supports direct sensor-to-FPGA interfacing for lower-latency AI processing. As shown in Figure 1, available interfaces include true differential signaling (TDS), MIPI D-PHY, PCIe 3.0, 10GbE Ethernet, and LPDDR4.
• Built for AI: The logic fabric includes AI tensor blocks and advanced digital signal processing (DSP) capabilities to support high-performance AI inferencing. This integration brings a tight coupling between the logic fabric and the AI capabilities, reducing the time and latency in the AI system. Using the tensor mode for AI enables 20 INT8 operations per clock cycle, a 5x improvement over previous DSP block architectures.
• Optimal memory hierarchy: Agilex 3 FPGAs offer a memory hierarchy well suited for AI applications including: small MLAB memory and larger M20K block RAM connected to the AI tensor blocks for storing model weights and compute results.  In addition, hardened LPDDR4 controllers allow simplified interfacing to larger off chip data buffers.
• Integrated processor: A built-in dual Arm Cortex-A55 processor allows the FPGA to operate as a standalone AI processing unit in many applications, eliminating the need for a host CPU.

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.

Getting Started with Agilex 3 FPGAs

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:

• Two DisplayPort 1.4 connectors supporting up to 8K video,
• Two MIPI connectors for interfacing with mobile device cameras and displays,
• One Pmod connector and one Raspberry Pi HAT connector for common development peripherals, and
• Two LPDDR4 2GB memory modules for efficient performance.
• Optional expansion card supporting PCIe 3.0 x1

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)

FPGAs Expand the Possibilities for Edge AI

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.
When not inspiring large B2B tech audiences to action, Brandon coaches Phoenix-area sports franchises through the TV.