Efficient edge AI is in reach

Businesses recognize the diverse benefits of investing in and embracing edge AI deployments. Edge sensor and device sales exploded in 2024, with half-a-billion edge devices shipping out to buyers in 2024. This excitement over edge AI’s transformative capabilities has experts estimating that the market for these products will grow tenfold from $27B to nearly $270B by 2032.

However, powering and connecting such complex, distributed ecosystems is challenging. These ecosystems are often subject to significant space and power constraints, all of which hinder their capacity to integrate advanced AI models. Designers need flexible, capable, and tailored hardware to build systems that are up to the task.

Efficiency at the edge

Edge systems eliminate the need for source-to-cloud transmission of full datasets, instead opting to process and prioritize data as close to its origin as possible. This is done through a network of sensors including cameras, temperature monitors, and other devices as in cloud-based builds, but with one key difference: Edge systems process data on-site, at the source, relieving strain on the central server.

As a result, the devices within the system need adequate compute functionality to run the AIs that allow them to offload processing tasks from the central server. Adding this functionality to IoT devices is possible, but it’s not easy. Balancing space, power demand, and performance within traditional IoT devices’ limited footprints is difficult enough, and adding AI only exacerbates these challenges. 

CNNs, LLMs, VLMs, and other popular models are large and resource-hungry. Though tailoring these models to role-specific functionalities can help mitigate power and processing demands, it can’t solve the problem in isolation. 

There are also other factors to consider. Despite the power savings associated with reduced communication with cloud servers, edge AI devices are expected to be “always-on,” driving significant energy needs over time. Further, this demand grows as systems and desired functionalities do; as systems become more capable and cover more ground, additional devices add to need and existing devices may need to change, too.

FPGAs: Enabling next-generation edge

This is where Field Programmable Gate Arrays (FPGAs) come into play. These specialized semiconductors have proven to be powerful enablers of advanced edge AI systems, especially when used as secondary chips. In this context, FPGAs serve not as the “brain” of the system but as the interface that supports interconnection and high-volume processing tasks. They are well-suited to this configuration because of their wide range of I/O compatibility and fast inferencing, which supports deterministic communication for closed-loop systems.

Where FPGAs really shine, though, are in high-stakes, low power builds. Their small footprint, parallel processing capabilities, and low power draw enable complex computing without sacrificing performance or efficiency. They are also flexible enough to perform a wide range of functions at the edge, ensuring that only necessary data gets passed along to central servers. 

Further, they are high efficiency components with adaptable power and performance controls that enable FPGA-based devices to move between ambient, middle, and high-performance processing based on designer-defined contexts. This is a perfect combination for battery-powered devices that may be deployed for extended periods without easy access to supplemental power, like a security camera in a remote area.

FPGAs are also highly adaptable and secure, both of which are critical in today’s edge AI investments. In-built security features enable FPGAs to serve as a hardware root of trust (HRoT) to ensure that sensitive data is protected even if gaps in software security are identified, while more compute means fewer transfers and less risk of exposure through interception. Built with flexibility in mind, FPGAs are also reprogrammable to future-proof investments by enabling changes as the system scales and business needs shift.

Smart, secure, and at the source

As the market for edge devices continues to grow, the ability to process data close to its source will not only enhance real-time analysis and response but also bolster security and adaptability. Being able to support these devices in a manner that mitigates power demand and consumption will guarantee the most sustainable edge success. FPGAs are key enablers when building AI-ready edge devices, helping to unlock faster and more efficient systems that drive safety, quality, and innovation.

About the author

Hussein Osman, segment marketing director at Lattice Semiconductor.
Hussein Osman is a semiconductor industry veteran, with 20+ years’ experience bringing multiple new technologies to market. He leads the sensAI solution development and go-to-market at Lattice Semiconductor.

Article Topics

AI/ML  |  edge AI  |  edge computing  |  FPGA  |  IoT devices  |  Lattice Semiconductor