Speaker
Description
The analysis of point cloud data, for example signals from charged particles recorded by detectors in high energy physics (HEP) experiments, can be significantly enhanced and accelerated by the application of machine learning models. In recent years, transformer architectures have come into focus as offering excellent model performance. However, for traditional transformers,the need to compute attention between all elements of the input data set results in high computational requirements and poor scaling of the inference performance with increasing data set size. To address this, the Locality-Sensitive Hashing-Based Efficient Point Transformer (HEPT) has been proposed, which segments the input dataset into smaller samples based on their adjacency which is evaluated using a hashing function. This approach has been shown to greatly improve computational efficiency when deployed on traditional GPU architectures.
For deployment with stricter latency requirements, for example in the trigger systems of HEP experiments, further accelerating the inference of the HEPT architecture is required. We present an implementation of HEPT for AMD/Xilinx FPGAs using hls4ml, which includes the hashing and segmentation of the data set, the attention computation, and recombination of the data. Using a charged particle track reconstruction model as the benchmark, latencies on the microsecond scale are achieved within the computing resources available on an Alveo u250 FPGA. Model compression using pruning and quantization with PQuant is explored.