A Modular VHDL Framework for Deterministic FPGA Acceleration of MR Fingerprinting Reconstruction

Abstract

Neural networks (NNs) for real-time medical image processing in resource-constrained environments benefit from hardware platforms that ensure deterministic timing, low latency, and fine-grained control over computational resources. This work introduces a fully custom and modular hardware framework for implementing fully connected neural networks (FCNs) on FPGAs, targeting online quantitative MR fingerprinting (MRF) reconstruction without relying on high-level synthesis tools. The proposed approach is based on a reusable base node that integrates numerical operations within a compact, parameterizable hardware unit. A single block of 16 such nodes is instantiated and reused in time multiplexing over a fixed schedule, so that the number of clock cycles of an inference is a property of the design rather than of the data. The framework is validated through a three-way comparison: the proposed custom accelerator, three hls4ml-generated designs, and the reference software implementation. The complete network was executed on an AMD/Xilinx Alveo U250 board over a 5000-sample dataset covering a broad range of T1 and T2 values: the accelerator agrees with the reference 8-bit quantized model on every one of the integer outputs, so that the residual error of the deployed system, a mean absolute percentage error (MAPE) of 2.37% on T1 and 11.07% on T2 with respect to the ground truth, is entirely that of the quantized network and not of the hardware. Timing closure at 83.3 MHz and post-implementation analysis indicate a 103 speed-up over CPU execution, and an estimated energy reduction of 104, with limited FPGA resource usage and approximately 8.6 W power consumption. None of the three hls4ml configurations tested reached a working bitstream on the target device, while the proposed architecture completed place-and-route and was executed on hardware, supporting its use as a building block for online quantitative MRI reconstruction.