As AI continues to expand from cloud computing to edge devices, developers face increasing challenges in balancing power efficiency and performance. The evolving demands of AI applications require adaptable solutions, particularly at the edge, where high-end processors may not always be viable due to power constraints. Field-programmable gate arrays (FPGAs), known for their flexibility and low power consumption, are emerging as a key technology to address these needs.

FPGA’s Role in a Changing AI Landscape

The rapid growth of AI applications at the edge and in the cloud has led to increasing demand for solutions that can quickly adapt to market changes. Unlike fixed-function hardware such as application-specific integrated circuits (ASICs), FPGAs offer a reconfigurable platform that enables developers to refine designs even during mass production. This adaptability is particularly valuable in industries ranging from consumer electronics and automotive to defense, medical applications, and cloud data centers.

According to Zhengfan Xie, Applications Director at Lattice Semiconductor, technology plays a crucial role in differentiating products across various domains. “FPGA is widely FPGA applicable in multiple industries, including the growing fields of cloud and edge AI. Its flexibility allows businesses to adjust designs as needed, keeping pace with rapidly evolving technologies,” said Xie.

One advantage of FPGAs is their ability to be placed closer to edge sensors, enabling efficient data processing and real-time analytics. This capability helps developers accelerate time-to-market while maintaining design flexibility. On the cloud side, FPGAs also offer solutions for post-quantum cybersecurity challenges. “If new encryption algorithms emerge, FPGA can quickly implement them, ensuring fast and secure product deployment,” Xie noted.

FPGA’s Competitive Edge in AI Inference

The AI market has seen significant investment in cloud computing and client-side processing, with forecasts indicating a major expansion of AI applications at the network edge by 2025. Compared to dedicated AI processors like ASICs and GPUs, FPGAs offer a unique advantage by providing early-stage design flexibility, making them ideal for AI algorithm development before transitioning to ASICs for cost efficiency in mass production.

Xie highlighted the strengths of FPGAs in AI inference. “AI has two major functions: training and inference. While FPGAs are not suited for training, they excel in inference by integrating preprocessing and inference stages within a streamlined data pipeline.” He also pointed out that FPGAs have significant power efficiency advantages over GPUs, making them well-suited for energy-sensitive edge applications.