F3: An FPGA-Based Transformer Fine-Tuning Accelerator With Flexible Floating Point Format

Abstract

Transformers have demonstrated remarkable success across various deep learning tasks. However, their inference and fine-tuning require substantial computation and memory resources, posing challenges for existing hardware platforms, particularly resource-constrained edge devices. To address these limitations, we propose F³, an FPGA-based accelerator for transformer fine-tuning. To reduce computation and memory overhead, this paper proposes a flexible floating point (FFP) format which consumes fewer resources than traditional floating-point formats of the same bitwidth. We adapt low-rank adaptation to FFP format and propose a fine-tuning strategy named LR-FFP which reduces the number of trainable parameters without compromising fine-tuning accuracy. At the hardware level, we design specialized processing elements (PEs) for the FFP format. The PE maximizes the utilization of DSP resources, enabling a single DSP to perform two multiply-accumulate operations per cycle. The PEs are organized into a systolic array (SA) to efficiently handle general matrix multiplication during fine-tuning. Through theoretical analysis and experimental evaluation, we determine the optimal dataflow and SA parameters to balance performance and resource consumption. We implement the architecture on the Xilinx VCU128 FPGA platform and F³ achieves a performance of 8.2 TFlops at 250 MHz. Compared with CPU and GPU implementations, F³ achieves speedups of $15.22 \times $ and $3.44 \times $ , respectively, and energy efficiency improvements of $70.52 \times $ and $9.44 \times $ .