High-Performance FPGA Acceleration for Transformer-Based Models

Foundation neural networks—large-scale, pre-trained models such as transformers—have rapidly emerged as the cornerstone of state-of-the-art artificial intelligence systems across natural language processing, vision, multi-modal understanding, and beyond. These models, characterized by billions of parameters and intricate computational graphs, demand unprecedented levels of compute, memory bandwidth, and energy efficiency, especially during inference at scale. While GPUs and TPUs have been the dominant hardware platforms supporting these models, their limitations in power efficiency, customization, and deterministic latency have motivated the exploration of alternative accelerators. Field-Programmable Gate Arrays (FPGAs) have recently gained significant attention as a viable solution due to their reconfigurability, fine-grained parallelism, and ability to tailor hardware directly to model-specific computations. However, deploying foundation models efficiently on FPGAs remains an enormously challenging task due to a multitude of hardware-software co-design complexities, memory hierarchy limitations, toolchain immaturity, and a lack of abstraction layers suited for the evolving model landscape.This paper provides a comprehensive and in-depth review of FPGA-based accelerators for foundation neural networks, systematically examining the current architectural techniques, optimization strategies, and deployment methodologies that have been proposed in the literature and industry. We begin by surveying the fundamental challenges in mapping high-dimensional tensor operations—especially those involved in attention mechanisms, normalization layers, and large-scale matrix multiplication—onto FPGA fabrics, and discuss techniques such as systolic arrays, dataflow execution, quantization, sparsity exploitation, and operator fusion that mitigate these challenges. We analyze representative accelerator architectures and demonstrate how different design trade-offs influence performance, power efficiency, and scalability across various FPGA platforms. Furthermore, we explore the critical role of compiler and toolchain ecosystems in enabling efficient model-to-hardware transformations, identifying the current bottlenecks and highlighting emerging frameworks aimed at closing the productivity gap.Beyond the state-of-the-art, we delve into unresolved technical barriers including on-chip memory constraints, dynamic sequence handling, design space exploration (DSE) complexity, and limitations in runtime adaptability. We also discuss how FPGAs can be integrated into larger heterogeneous systems, including hybrid FPGA-GPU architectures and cloud-based FPGA-as-a-Service platforms, to support full-scale deployment pipelines for foundation models. Particular attention is given to the emerging paradigm of model-hardware co-design, where foundation models are trained with explicit consideration of hardware constraints to maximize efficiency and deployability. Finally, we outline key future directions, including ultra-low-precision arithmetic, reconfigurable attention kernels, FPGA-friendly model architectures, and domain-specific compilers that may fundamentally reshape the design landscape of foundation model accelerators.Through this review, we aim to provide a detailed roadmap for researchers, engineers, and system architects seeking to harness the potential of FPGA platforms for foundation model inference and beyond. By bringing together insights from machine learning, hardware architecture, and systems engineering, we highlight not only the promise but also the rigorous interdisciplinary efforts required to make FPGA-based AI acceleration viable, scalable, and accessible in the era of ever-growing foundation models.