In the AI era dominated by GPU acceleration, Field-Programmable Gate Arrays (FPGAs) occupy an ambiguous position. While FPGAs offer the promise of custom hardware acceleration and reconfigurability, they face fundamental challenges when deployed as standalone accelerators for AI workloads. The rapid evolution of GPU technology—with each generation delivering massive improvements in HBM bandwidth, memory capacity, and compute throughput—has left FPGAs struggling to find their niche in modern AI infrastructure. However, this narrative is beginning to shift. Rather than competing directly with GPUs, FPGAs may find their greatest value in co-design architectures that leverage pipeline parallelism between FPGA and GPU. By offloading specific operators to dedicated FPGA hardware, we can reduce GPU performance interference, enhance cache locality, and achieve custom acceleration for specialized computations. This hybrid approach represents a promising direction for FPGA deployment in production AI systems.
Why Standalone FPGA Struggles
When evaluated as standalone accelerators for AI inference or training, FPGAs face several fundamental limitations that make them difficult to justify compared to modern GPUs.
The most critical limitation is the memory subsystem. Modern GPUs have achieved extraordinary memory bandwidth through High Bandwidth Memory (HBM). A modern GPU like the H100 can achieve around 3 TB/s of memory bandwidth with HBM3, while high-end FPGAs typically offer 100-200 GB/s with DDR4 or limited HBM support (820 GB/s for HBMe2 in Xilinx Versal). This 3-30× bandwidth gap is particularly problematic for AI workloads, which are often memory-bandwidth bound. Large Language Models require frequent access to model weights (a 7B model needs ~14 GB, a 70B model needs ~140 GB), and the iterative nature of inference means that memory bandwidth often caps throughput rather than compute capability. For FPGAs, even when equipped with HBM, the available bandwidth is typically a fraction of what GPUs offer. This fundamental limitation means that FPGAs cannot efficiently serve as drop-in replacements for GPUs in most AI workloads.
GPU compute capability has advanced at a pace that FPGAs struggle to match. GPU generations from Volta to Ampere to Hopper have delivered 2-3× performance improvements every 2-3 years, while FPGA generations advance more slowly, with improvements focused on capacity and efficiency rather than raw throughput. Specialized tensor cores in modern GPUs provide massive acceleration for matrix operations that are central to AI workloads. While FPGAs can theoretically achieve high performance through custom logic design, the engineering effort required to match GPU performance for general AI workloads is often prohibitive. The programmability advantage of FPGAs is offset by the need for extensive RTL development and optimization.
Beyond raw performance, FPGAs face practical challenges in development and deployment. RTL design and verification take significantly longer than GPU kernel development. GPU frameworks like PyTorch, TensorFlow, and CUDA have mature toolchains, while FPGA toolchains are more fragmented. FPGA bitstream management, partial reconfiguration, and runtime updates are more complex than GPU kernel updates. These factors make it difficult to justify FPGA deployment for general-purpose AI workloads where GPUs already excel.
Network Acceleration Falls Short
One area where FPGAs were expected to excel is network acceleration—offloading network processing, protocol handling, and data movement from CPUs. However, the reality has been more nuanced and often disappointing.
FPGAs seemed well-suited for network acceleration because hardware-level packet processing could reduce CPU overhead, they could implement specialized network protocols in hardware, and they offered deterministic performance with predictable latency for real-time applications. However, several factors have limited FPGA adoption in network acceleration. Modern CPUs with high core counts and advanced instruction sets can handle network processing efficiently. Purpose-built SmartNICs like NVIDIA BlueField and Intel IPU have emerged as more integrated solutions. The trend toward software-defined networking reduces the need for hardware-level protocol customization. The development and deployment costs of FPGA-based network acceleration often don’t justify the marginal performance gains.
For AI workloads specifically, FPGA-based network acceleration has shown limited benefit. Modern GPUs can communicate directly through NVLink and PCIe without significant CPU/network bottlenecks. While in-network computing is interesting, it has not shown clear advantages for typical AI serving patterns. The overhead of network protocols is often negligible compared to compute and memory access costs in AI workloads. The network acceleration use case, while technically feasible, has not emerged as a compelling reason to deploy FPGAs in AI infrastructure.
FPGA+GPU Co-Design Through Pipeline Parallelism
If standalone FPGA deployment is challenging and network acceleration is lackluster, where does FPGA fit in the AI era? The answer lies in co-design architectures that combine FPGA and GPU through pipeline parallelism.
Rather than using FPGA as a replacement for GPU, we can use it as a complementary accelerator that handles specific stages of the AI inference pipeline. The key insight is that not all operators in AI workloads are equally suited for GPU execution. Some operators benefit from GPU’s massive parallelism and high memory bandwidth, while others may have characteristics that make them better suited for FPGA’s custom hardware approach: irregular memory access patterns, bit-level or low-precision operations, custom data transformations, or operations that cause GPU resource contention.
In a pipeline parallelism setup, the AI inference pipeline is divided into stages. For example, input data might flow through an FPGA stage, then a GPU stage, then another FPGA stage, and finally another GPU stage before producing output. Each stage processes data and passes results to the next stage. The FPGA and GPU can operate concurrently on different data items, maximizing overall throughput.
The critical question is which operators should migrate to FPGA. Our exploration has focused on identifying operators that exhibit characteristics favorable to FPGA, such as custom bit manipulations or low-precision arithmetic, irregular control flow that doesn’t map well to GPU SIMD, small working sets that fit in FPGA on-chip memory, or operations that cause GPU cache pollution. We also look for operators that reduce GPU performance interference—those that compete with main computation for GPU resources, memory-intensive operations that saturate GPU memory bandwidth, or operations that fragment GPU memory allocation. Finally, we consider operators that benefit from custom hardware acceleration: operations that can be highly optimized in custom logic, transformations that require specialized data paths, or preprocessing/postprocessing that doesn’t need GPU’s full capabilities.
Reducing GPU Interference and Enhancing Locality
Our exploration of FPGA+GPU pipeline parallelism has revealed several concrete benefits that justify this hybrid approach.
One of the most significant benefits is reducing interference on GPU resources. In production AI serving systems, GPUs often run multiple workloads concurrently, leading to memory bandwidth contention where multiple operators compete for the same memory subsystem, cache pollution where operators with poor locality evict useful data from GPU caches, and SM resource contention where different operators compete for compute resources. By offloading specific operators to FPGA, we can isolate resource usage so FPGA operators don’t compete with GPU’s main computation, reduce memory pressure by freeing memory bandwidth for core computations, and improve GPU utilization by allowing the GPU to focus on operations it excels at, such as large matrix multiplications. This isolation is particularly valuable in multi-tenant serving environments where resource interference is a major concern.
FPGAs can improve cache locality in several ways. FPGAs have distributed on-chip memory (BRAM/URAM) that can hold small working sets entirely on-chip, eliminating off-chip memory access. FPGA can preprocess data into formats that are more cache-friendly for GPU consumption. By moving certain operators off GPU, we reduce the number of memory access patterns competing for GPU cache space. For example, if an operator performs many small, scattered memory accesses that don’t benefit from GPU’s cache hierarchy, moving it to FPGA where we can design custom memory access patterns can improve overall system performance.
FPGAs excel at custom hardware acceleration for specialized operations. They can implement custom number formats like 4-bit or 8-bit arithmetic with hardware optimized for those specific bit widths. Operations that require fine-grained bit manipulation are natural fits for FPGA. FPGA allows designing data paths specifically optimized for particular transformation patterns. For real-time applications, FPGA can provide predictable, low-latency execution. These custom accelerations can provide performance improvements that are difficult or impossible to achieve on general-purpose GPU hardware.
Beyond individual operator performance, FPGA+GPU co-design offers system-level advantages. Each accelerator handles the workload it’s best suited for, leading to better resource utilization. We can scale FPGA and GPU resources independently based on workload characteristics. FPGA can be reconfigured for different operator sets as workloads evolve. This approach may enable using smaller or cheaper GPUs by offloading certain operations, improving cost efficiency.
Challenges in Co-Design
While FPGA+GPU co-design is promising, it introduces several challenges that must be addressed.
Moving data between FPGA and GPU introduces overhead. Data must traverse PCIe between FPGA and GPU, typically at 16-32 GB/s, which is much lower than on-chip bandwidth. Each stage transition adds latency to the pipeline. Pipeline stages must coordinate to avoid stalls. These overheads must be carefully managed to ensure that the benefits of offloading outweigh the costs of data movement.
Determining the optimal partitioning strategy is non-trivial. We need to decide which operators should run on FPGA versus GPU, how to balance pipeline stages to avoid bottlenecks, and how to handle dynamic workloads where operator characteristics change. This requires sophisticated profiling, analysis, and potentially runtime adaptation.
Co-design architectures increase complexity. They require expertise in both FPGA and GPU development, more complex debugging and performance tuning, and additional deployment and management overhead. However, these challenges may be justified if the performance and efficiency gains are substantial.
Future Directions
The future of FPGA in AI infrastructure is not as a standalone replacement for GPUs, but as a specialized co-processor in hybrid architectures. As AI workloads become more diverse and specialized, the ability to offload specific operations to custom hardware becomes increasingly valuable.
Key research directions include automated operator migration tools that automatically identify FPGA-suitable operators and generate optimized implementations, dynamic pipeline reconfiguration systems that adapt FPGA configuration based on workload characteristics, tight integration architectures that minimize FPGA-GPU data movement overhead through closer integration, and domain-specific optimizations with specialized FPGA designs for emerging AI workloads such as sparse models, quantization, and specialized attention mechanisms.
The lessons learned from exploring FPGA+GPU co-design will be valuable as we build the next generation of AI infrastructure that must efficiently handle increasingly diverse and specialized workloads.
FPGA’s role in the AI era is not as a direct competitor to GPUs, but as a complementary accelerator in co-design architectures. While standalone FPGA deployment faces fundamental limitations in memory bandwidth, compute throughput, and development complexity, FPGA+GPU pipeline parallelism offers a promising path forward.
By identifying FPGA-suitable operators and offloading them to dedicated hardware, we can reduce GPU performance interference, enhance cache locality, and achieve custom acceleration for specialized computations. This hybrid approach represents a pragmatic and potentially high-impact direction for FPGA deployment in production AI systems.
The future of AI infrastructure will likely involve diverse accelerators working together, each optimized for different aspects of the workload. Understanding how to effectively combine FPGA and GPU through pipeline parallelism provides crucial insights for building efficient, scalable AI systems that can adapt to the evolving demands of modern AI workloads.
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