This paper presents the first demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physics experiments. We present a three-fold contribution, with the PicoCal calorimeter, planned for the LHCb Upgrade II experiment, used as a test case. First, we develop a lightweight autoencoder to compress a 32-sample timing readout, representative of that of the PicoCal, into a two-dimensional latent space. Second, we introduce a systematic, hardware-aware quantization strategy and show that the model can be reduced to 10-bit weights with minimal performance loss. Third, as a barrier to the adoption of on-detector ML is the lack of support for radiation-hard FPGAs in the High-Energy Physics community's standard ML synthesis tool, hls4ml, we develop a new backend for this library. This new back-end enables the automatic translation of ML models into High-Level Synthesis (HLS) projects for the Microchip PolarFire family of FPGAs, one of the few commercially available and radiation hard FPGAs. We present the synthesis of the autoencoder on a target PolarFire FPGA, which indicates that a latency of 25 ns can be achieved. We show that the resources utilized are low enough that the model can be placed within the inherently protected logic of the FPGA. Our extension to hls4ml is a significant contribution, paving the way for broader adoption of ML on FPGAs in high-radiation environments.

翻译：本文首次演示了在FPGA上实现可行、超快、抗辐射的机器学习应用，该应用可用于未来高能物理实验。我们以计划用于LHCb升级II实验的PicoCal量能器作为测试案例，提出三重贡献。首先，我们开发了一种轻量级自编码器，将代表PicoCal的32样本定时读数压缩到二维潜在空间。其次，我们引入了一种系统化的硬件感知量化策略，并证明模型权重可缩减至10位且性能损失最小。第三，针对探测器端机器学习应用面临的主要障碍——高能物理领域标准ML综合工具hls4ml缺乏对抗辐射FPGA的支持，我们为该库开发了新的后端。该后端能够将ML模型自动转换为针对Microchip PolarFire系列FPGA的高级综合项目，这是少数商用抗辐射FPGA之一。我们在目标PolarFire FPGA上实现了自编码器综合，结果表明可实现25纳秒的延迟。所用资源足够少，模型可部署在FPGA的固有保护逻辑区域内。我们对hls4ml的扩展是一项重要贡献，为在高辐射环境中更广泛地采用基于FPGA的机器学习铺平了道路。