The integrated adoption of next-generation artificial intelligence (AI) at the Edge is rapidly accelerating within modern enterprise environments. According to IDC, organizations spent more than $16 billion on generative AI in 2023 and will expand investments to more than $143 billion over the next three years. Another IDC report forecasted that Edge computing investments will exceed $232 billion in 2024, an increase of 15.4% from 2023, to help facilitate the deployment of advanced AI applications. It’s increasingly clear that Edge AI computing is a focal point of the future of innovative automation.
Power efficiency is critical for always-on Edge AI devices to operate efficiently. FPGAs are adept at delivering high performance with lower power consumption compared to traditional processors, making them ideal for achieving scalable performance and designs in Edge AI computing environments.
Real-time AI inference with low latency and energy usage is made possible by FPGAs’ reconfigurable nature and parallel processing capabilities. Since they are hardware-based and perform computations in parallel, they minimize energy. Most Edge AI applications are reliant on smart cameras and sensors that produce high volumes of data. Leveraging dynamic parallel processing, FPGAs seamlessly aggregate that camera and sensor data so that applications can quickly analyze large information sets. Consolidating and processing multiple data streams simultaneously surpasses traditional CPUs that only analyze one batch of data at a time.
By processing data in parallel, FPGAs serve as key facilitators for Edge AI applications that react to real-time changes in their environment. Take autonomous vehicles for example. FPGAs deliver strong situational awareness functionality that accelerates on-board AI computations for object detection, path planning, and real-time decisioning in autonomous robots.