Enabling low-latency machine learning on radiation-hard FPGAs with <tt>hls4ml</tt>

Abstract This paper presents an end-to-end 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 Large Hadron Collider beauty 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 projects for the Microchip PolarFire family of FPGAs, one of the few commercially available and radiation hard FPGAs. We present