HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference

TL;DR

HGQ-LUT is a novel LUT-aware training approach for DNNs that significantly accelerates training and enhances hardware efficiency, enabling practical deployment on FPGA-based systems.

Contribution

It introduces a new LAT method with accelerator-efficient layers, automated accuracy-resource trade-off exploration, and integration into open-source tools for real-world FPGA deployment.

Findings

Achieves over 100x faster training on GPUs.

Provides state-of-the-art hardware efficiency for LUT-based DNNs.

Enables automated design and verification of hybrid architectures.

Abstract

Lookup-table (LUT) based neural networks can deliver ultra-low latency and excellent hardware efficiency on FPGAs by mapping arithmetic operations directly onto the logic primitives. However, state-of-the-art LUT-aware training (LAT) approaches remain difficult to use in practice: they are often orders of magnitude slower to train than conventional networks, require non-trivial manual tuning for hardware efficiency, and lack an end-to-end workflow. This work presents HGQ-LUT, integrated in https://github.com/calad0i/HGQ2, a new LAT approach that achieves state-of-the-art hardware efficiency while accelerating training by over 100 times on modern GPUs. HGQ-LUT introduces LUT-Dense and LUT-Conv layers that are implemented with regular, accelerator-efficient tensor operations during training, which are then compiled into logic LUTs for hardware. By combining these layers with fine-grained, element-wise heterogeneous quantization (including zero-bit pruning) and a LUT-aware resource surrogate, HGQ-LUT enables the automatic exploration of accuracy-resource trade-offs without manual bit-width tuning. We further integrate HGQ-LUT into open-source toolchains, enabling unified design, compilation, and bit-exact verification of hybrid architectures that mix LUT-based with conventional arithmetic blocks. These features make LAT-based DNNs practical for real-world deployment, such as at the CERN Large Hadron Collider's experiments.