This year marks the 40th anniversary of the first commercially available Field-Programmable Gate Array (FPGA), which introduced the concept of reprogrammable hardware. By creating “hardware as flexible as software,” the reprogrammable logic of FPGAs has fundamentally changed the landscape of semiconductor design. For the first time in history, developers could design chips and redefine their functionality to perform different tasks even mid-design or after manufacturing, should specifications or requirements change. This flexibility has accelerated the development process of new chip designs, shortened time-to-market for new products, and provided an alternative to Application-Specific Integrated Circuits (ASICs).
The impact of FPGAs on the semiconductor market has been extraordinary. It has spawned an industry worth over $10 billion. Over the past forty years, Xilinx (now acquired by AMD) alone has delivered over 3 billion FPGAs and Adaptive System-on-Chips (SoCs, which combine FPGA architecture with system-on-chip and other processing engines) to more than 7,000 customers from various market sectors.
Accelerating Innovation
FPGAs were invented by Ross Freeman, co-founder of Xilinx. As an engineer and innovator, he believed there must be a better and more cost-effective way to design chips beyond standard fixed-function ASIC devices. FPGAs provide engineers with the freedom and flexibility to make instant changes to chip designs, enabling them to develop and design custom chips within a day. Furthermore, FPGAs have driven the rise of the “fabless” business model, which has fundamentally transformed the entire semiconductor industry. By eliminating the need for custom mask tools and associated one-time engineering costs, FPGAs have demonstrated to the industry that companies can create groundbreaking hardware without owning a fabrication plant—they only need vision, design capability, and an FPGA, thus accelerating hardware innovation.
Since the launch of the world’s first commercial FPGA (XC2064) 40 years ago, FPGAs have become ubiquitous in electronics and deeply integrated into daily life. Today, adaptive computing devices (including FPGAs, adaptive SoCs, and System-on-Modules (SOMs)) are widely used across various fields, from automotive, trains, and traffic lights to robotics, drones, spacecraft, satellites, wireless networks, medical and testing equipment, smart factories, data centers, and even high-frequency trading systems.
In the current wave of digital transformation, edge computing and artificial intelligence are leading the technological revolution. FPGAs are gradually becoming a key driver in the field of edge AI.
Artificial Intelligence in Edge Computing
Currently, most AI workloads run on GPUs in data centers. However, an increasing amount of AI processing is occurring at the edge. FPGA technology is at the forefront of the rapid growth of AI integration applications across industries. Edge AI—deploying AI models on edge devices for localized algorithm processing rather than relying on centralized computing platforms like the cloud—has become one of the fastest-growing directions in the AI field, garnering significant attention from the industry. It is estimated that the edge AI market will reach approximately $21 billion by 2024 and is expected to exceed $143 billion by 2034. This growth trend indicates that various industries will continue to increase their investment in AI-based edge system R&D.
FPGAs and SoCs can perform low-latency real-time processing of sensor data, enabling accelerated AI inference at the edge. With the recent introduction of smaller generative AI models, we can see a “ChatGPT moment” extending to the edge—these new AI models can run on edge devices, whether in AI personal computers, vehicles, factory robots, space equipment, or any embedded applications.
Compared to GPUs and NPUs, FPGAs have several advantages in edge AI applications: first, energy efficiency; FPGAs have lower power consumption and higher flexibility during the deep learning inference phase, making them suitable for evolving algorithm demands. In contrast, while GPUs are efficient in deep learning training models, they consume more power during inference, and although NPUs have high energy efficiency, they struggle to adapt to rapidly iterating AI models; second, flexibility; the hardware reconfigurability of FPGAs allows them to customize acceleration units for different algorithms, particularly suitable for scenarios requiring rapid iteration. GPUs and NPUs are relatively weaker in flexibility; third, versatility; GPUs are highly versatile and can handle various AI workloads, while NPUs exhibit overwhelming advantages in specific scenarios but have higher programming complexity; fourth, security; the hardware logic embedded in FPGAs is more difficult for attackers to tamper with than software implementations. Additionally, processing the AI inference process on edge FPGA devices can avoid transmitting sensitive data to the cloud, effectively reducing the risk of data leakage.
The application prospects for edge AI are broad and full of innovative opportunities, but developers still face unique challenges in practice. For example, the high development threshold; compared to writing AI software in languages like Python, FPGA programming has a higher technical barrier and longer development cycles. Secondly, resource limitations; FPGAs on edge devices are often smaller in scale and have limited resources, making it difficult to directly port complex deep learning models, requiring these models to be compressed, quantized, and structurally optimized to fit resource constraints. Furthermore, the ecosystem is not mature enough; compared to the mature ecosystem of GPUs in the AI field, the development toolchain, IP core libraries, and framework support for FPGAs still need improvement, and industry standards have yet to be unified.
Application Scenarios of FPGAs in Edge AI
It is certain that FPGAs have broad application prospects in edge AI in the future. For instance, intelligent video analysis in security monitoring and traffic management requires real-time analysis of large volumes of video streams. FPGA edge devices can process video data directly at the camera end, transmitting only analysis results rather than raw video, thereby alleviating network bandwidth pressure and improving processing timeliness.
The Industrial Internet of Things is also a major application field for FPGA edge devices. In factory workshops, predictive maintenance and quality control systems require millisecond-level response speeds. FPGA edge devices can process data streams from various sensors in real-time, identifying abnormal patterns before faults occur, significantly enhancing production efficiency and safety.
Portable medical diagnostic devices require efficient processing of complex biological signals. The low power consumption and high parallelism of FPGAs make them an ideal platform for medical AI applications such as ECG analysis and real-time pathological image processing.
In-vehicle AI systems also need to process data from various sensors, including radar, LiDAR, and cameras, and make decisions in a very short time. The parallel processing capability of FPGAs can be reconfigured to make them ideal components in such systems.
In Conclusion
The 40-year journey of FPGAs has been both challenging and solid, evolving from the initial idea of creating “hardware as flexible as software” to widespread applications across various industries. With their unique characteristics of low power consumption and reconfigurability, FPGAs have steadily driven development in various fields.
In the future, the edge AI field will be a major frontier for innovation. Future edge AI devices may adopt a heterogeneous architecture of CPU + GPU + FPGA + dedicated accelerators, leveraging the strengths of each. However, it is undeniable that while FPGAs have some “shortcomings,” they remain irreplaceable in the field of edge AI. Regardless of how AI models evolve or how edge scenarios develop, FPGAs will continue to thrive across various fields by constantly “refreshing themselves.”
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