Foreword: Over the past 40 years, the number of transistors in FPGAs has soared from 85,000 to 138 billion, and logic units have grown from 64 to 18.5 million, with over 3 billion units shipped cumulatively. This [veteran], born in 1985, though less well-known than some, has quietly risen in critical fields like artificial intelligence, 5G/6G communications, and medical technology, becoming the [invisible infrastructure] supporting the digital economy. Amidst collaboration and competition with ASICs and GPUs, FPGAs have carved out a unique path to prominence.

The Core Advantages of FPGAs: Indispensable in the AI Era

The essence of an FPGA is [reconfigurable hardware], allowing designers to reprogram or reconfigure digital logic post-deployment based on demand. This feature gives it a natural edge in rapidly evolving technological environments.

① Adapting to Algorithm Iteration: FPGAs can quickly match new algorithms through reprogramming, making them ideal during the algorithm exploration phase and when standards are undefined.

From real-time algorithm optimization in high-frequency trading to AI processing technology exploration in 6G networks, the programmability of FPGAs makes them an [insurance mechanism] against uncertainty.

② Balancing Performance and Cost: For small-batch, high-performance special needs, such as laboratory high-precision measurement equipment, FPGAs eliminate the need to bear the high fixed costs of ASICs, offering significant cost-effectiveness.

Although the cost per logic unit of an FPGA is slightly higher than that of an ASIC, its return on investment is higher in scenarios where algorithms are unstable and market sizes have not reached a critical threshold.

③ Low Power Consumption Value in Edge Computing Scenarios: Terminals like smart cameras, wearable devices, and industrial sensors require real-time data processing but are constrained by limited power supplies.

On the edge side, especially in operations requiring real-time decision-making without latency, FPGAs are the optimal choice.

New Application Scenarios for FPGAs Through Technological Breakthroughs

From traditional military and aerospace fields to emerging sectors like AI and 5G, the application boundaries of FPGAs are rapidly expanding.

According to market research, the global FPGA market size is projected to grow from $11.73 billion in 2025 to $19.34 billion in 2030, with a compound annual growth rate of 10.5%. The growth is primarily driven by six core application scenarios.

① AI Infrastructure: The [First Line of Defense] for Data Preprocessing

The performance of AI systems depends not only on the computing power of GPUs/CPUs but also on data transmission and preprocessing efficiency.

In data centers, FPGAs are becoming a key component of AI infrastructure.

They perform data cleaning, format conversion, noise reduction, and other preprocessing tasks before data reaches the CPU or GPU, alleviating memory and I/O bottlenecks.

Even with the most advanced LLM models, [garbage in, garbage out] will occur if the data is noisy.

FPGAs can handle diverse inputs like PPTs, voice, and text, converting them into standard formats for backend computing units.

In the mesh topology of data centers, FPGAs, as smart network cards, enable high-speed data movement and precise scheduling, maximizing the advantages of memory and computing unit proximity with their low-latency characteristics.

② Edge AI: [Real-Time Computing Power] from Robots to Medical Imaging

The demand for real-time decision-making on the edge side has found the perfect stage for FPGAs.

Robotics requires fusing heterogeneous data from cameras and sensors to complete path planning and obstacle recognition within milliseconds. The deterministic latency and sensor fusion capabilities of FPGAs make them the core hardware.

In medical imaging, from retinal scans to magnetic resonance imaging, massive analog data needs to be processed, and complex matrix operations are required. The AI engines of FPGAs excel in such computations.

FPGAs perform exceptionally well in processing raw analog data, filtering, and reconstructing images.

Their parallel computing architecture significantly enhances the processing speed of medical images, helping doctors detect early lesions faster.

In industrial quality inspection, intelligent transportation, and other scenarios, FPGAs can also achieve real-time data processing with low power consumption, becoming the [computing power core] of edge AI.

③ Communication Infrastructure: [Transition and Adaptation] in the 5G/6G Era

FPGAs have consistently played a crucial role in the evolution of wireless communication standards.

During the initial deployment of 5G/6G, when standards were not fully defined, equipment vendors like Erickson and Nokia could not develop ASICs in advance. FPGAs, with their reprogrammability, could adapt to changing protocol requirements, making them the preferred choice for early deployment.

The collaboration between Altera and BigCat aims to expand FPGA-based wireless access network technology.

In 5G beamforming applications, the use of AMD's adaptive SoCs has significantly increased. In the 6G field, where AI processing technology has not been standardized, the flexibility of FPGAs makes them the core platform for vendors to explore.

From the radio side to baseband equipment, FPGAs are required as auxiliary components. During the four to five-year deployment cycle of wireless standards, FPGAs are almost the only choice.

④ Cloud-Based Virtual FPGAs: [Offloading Engines] for Complex Computing

Cloud-based FPGA instances (such as AWS EC2 F2) are transforming the computing model in data centers.

These instances, equipped with PCIe cards carrying eight Xilinx FPGAs, can collaborate with the main processor via the PCIe bus, offloading compute-intensive workloads like DNA analysis and chemical reaction simulations from the CPU, resulting in faster processing speeds and lower energy consumption.

During its early startup phase, SiFive rented AWS FPGA boards at $6 per hour to build a low-cost simulator for verifying RISC-V designs, eliminating the need to purchase expensive FPGA prototype systems.

Cloud-based FPGAs can be quickly started and stopped, making them suitable for complex system verification and elastic computing power demands. This model is accelerating innovation in AI training and inference, bioscience, and other fields.

⑤ Horizontal Product Differentiation: [Implementation Path] for Customized Functions

For horizontally integrated semiconductor companies, fixed-function ASICs limit market coverage, while FPGAs help them achieve product differentiation.

By integrating embedded FPGAs in SoCs or supporting external FPGA connections, companies can provide customized hardware functions for different customers. This approach is faster and more energy-efficient than software implementation, without incurring the exclusive development costs of ASICs.

If you want a wearable device's battery to last a week, you need to transfer some functions from the processor to programmable logic. FPGAs can help companies optimize performance and power consumption without developing their own SoCs.

More importantly, embedded FPGAs can also protect the intellectual property of core algorithms through sparsity and obfuscation techniques, enabling software developers to achieve hardware acceleration without revealing secret algorithms.

⑥ Security Protection: [Programmable Barriers] Against Dynamic Threats

The frequent changes in network security standards and emerging threats like quantum hacking have raised higher requirements for hardware security.

FPGAs can quickly adapt to new security protocols through reprogramming. Their built-in hard encryption modules enable end-to-end online encryption, while AI-based packet detection functions can identify network threats in real-time. Through enhanced configuration and access control, FPGAs are becoming an important defense line in network security.

Collaboration Rather Than Replacement: The Ecosystem Scenarios of the Three

In today's diversification (diversified) computing power architecture, FPGAs, ASICs, and GPUs are not in a [either-or] competitive relationship but form a complementary and symbiotic hybrid ecosystem.

① FPGAs and ASICs: [Lifecycle Partners] in Dynamic Balance

The choice between the two essentially involves weighing technical maturity against market size. During the technology exploration phase, FPGAs undertake prototype verification and small-batch supply tasks.

When the market size expands and standards stabilize, companies can switch to ASICs to achieve cost and energy efficiency optimization while retaining some FPGAs to cope with standard changes.

This combination is particularly common in the communication field. During the initial deployment of 5G, equipment vendors used FPGAs to quickly respond to protocol iterations. When standards were defined, ASICs were mass-produced to reduce costs, while FPGAs were still used for flexible expansion functions in base stations.

② FPGAs and GPUs: [Computing Power Synergies] in Data Centers

GPUs excel in large-scale parallel computing and are the core of AI training. FPGAs, on the other hand, offer advantages in data preprocessing, task scheduling, and low-latency inference, significantly enhancing the overall efficiency of data centers.

FPGAs can aggregate GPU requests and intelligently sequence them, preprocessing data to alleviate I/O bottlenecks and allowing GPUs to focus on core computations.

③ Heterogeneous Computing: The Ultimate Positioning of FPGAs

As AI model sizes grow exponentially, a single chip finds it difficult to meet all needs. The heterogeneous integration of CPUs, GPUs, FPGAs, and ASICs has become an industry consensus.

FPGAs will play an increasingly important role alongside GPUs and CPUs, providing modular and flexible acceleration capabilities and supporting data processing close to the data source.

In future data centers, FPGAs will become the [computing power scheduling hub].

On the one hand, they optimize GPU efficiency through data preprocessing and task distribution. On the other hand, they bridge edge devices and cloud computing power, enabling end-to-cloud collaboration.

Market Landscape: Integration by Giants and Breakthroughs by Innovators

The FPGA market has long been dominated by a few giants. After AMD acquired Xilinx, it quickly rose in the data center and edge computing fields by leveraging the synergistic advantages of CPUs, GPUs, and FPGAs.

It has formed a dual-brand strategy for data centers and edges. The Alveo series focuses on cloud acceleration. The latest V80 acceleration card, featuring 7nm process technology and 8GB HBM2e memory, can achieve 1.2 trillion INT8 operations per second with its Transformer computing units optimized for LLM inference.

The Xilinx Versal series, on the other hand, emphasizes adaptive computing. Its ACAP architecture integrates AI engines, DSPs, and CPU heterogeneous cores.

In medical imaging reconstruction scenarios, it has reduced the processing time for 64-layer CT data from 8 seconds to 0.5 seconds, becoming a core supplier for companies like GE Healthcare and Siemens.

Meanwhile, innovative companies like ALINX are lowering the barriers to FPGA applications through modular design.

Its product system, consisting of core boards, function boards, FMC subcards, and IP cores, allows customers to quickly build industry solutions without developing hardware from scratch.

After Intel split Altera in 2025, it clearly defined its FPGA all-in-AI strategy, targeting the data center IPU, programmable network, and embedded intelligent terminal markets.

The Intel Agilex series FPGAs integrate AI tensor blocks. The Agilex 5 supports 56 INT8 TOPS of computing power, suitable for edge low-power scenarios.

The Agilex 7 breaks through the bottleneck of large model inference with HBM2e memory and is deeply integrated with the OpenVINO toolchain, lowering development thresholds.

Achronix has made a breakthrough with its stacked design and customization. Its Speedster7t FPGA, featuring TSMC's 7nm process technology and 2D NoC technology, supports GDDR6 and 400G Ethernet, demonstrating significant cost and energy efficiency advantages in LLM inference and becoming a differentiated choice for cloud vendors.

The domestic FPGA market has long been dominated by overseas vendors. However, driven by the dual forces of localization substitution policies and AI computing power demands, domestic companies are accelerating their rise.

Unigroup Guoxin launched the Logos series FPGAs. The LX7 series has a logic capacity exceeding 1 million LUTs and supports PCIe Gen4 and HBM2. It has been applied in domestic servers and industrial control equipment, with revenue increasing by 60% year-on-year in 2024.

Jing Micro Joint efforts (Qilite) focuses on edge AI scenarios. The JM7200 FPGA has an energy efficiency ratio of 0.6 TOPS/W, suitable for intelligent driving and IoT terminals, and has received orders from XPENG Motors and Hikvision.

Anlu Technology released the PHOENIX series of mid-to-high-end FPGAs, supporting INT8/FP16 mixed-precision computing. It has entered the data center inference pilot phase and plans to launch 7nm process products in 2025.

Fudan Microelectronics also has years of accumulation in the FPGA field. In the first half of 2025, the company's revenue from FPGAs and other products was 681 million yuan, accounting for 36.98% of total revenue.

The company stated that it will continue to enrich its FPGA product series to meet the market demand for new-generation FPGA products in artificial intelligence and digital communications.

Conclusion:

The future of FPGAs is not just about technological iteration of the chip itself but also about the reconstruction and innovation of computing power architecture.

In this computing power revolution, where [flexibility] wins, FPGAs are writing their own era chapter with their unique value.

When breakthroughs form a synergy, FPGAs will truly complete their transformation from reconfigurable chips to AI infrastructure.

Partial reference sources: FPGA Development Circle: [In the High-Speed AI Era, FPGAs Are Quietly Taking Over More Critical Workloads], AI FPGA Love: [Intelligent Computing Power Engine: How FPGAs Ignite the GPU Pooling Revolution in Data Centers], ALINX: [FPGA Outlook: An Inevitable Choice for High-Growth Vertical Fields, What Values ALINX Can Provide]