Fast inference of deep neural networks in FPGAs for particle physics (1804.06913v3)

Abstract: Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA hardware has only just begun. FPGA-based trigger and data acquisition (DAQ) systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. We develop a package based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

• The paper presents a case study using FPGAs to perform rapid jet substructure classification in LHC experiments.
• It details an implementation that employs network compression, fixed-point quantization, and parallel processing to maintain low latency and efficient resource usage.
• The research introduces hls4ml as a tool to simplify FPGA deployment, potentially democratizing access to advanced on-detector data processing in high-energy physics.

Overview of FPGA-Based Neural Network Inference in Particle Physics

This paper explores the implementation of neural network inference on Field Programmable Gate Arrays (FPGAs) with a specific focus on applications within particle physics, particularly within the context of the Large Hadron Collider (LHC). The research addresses the potential for these technologies to enhance the real-time data processing capabilities critical for triggering and data acquisition systems in high-energy physics experiments.

FPGAs are well-suited for environments such as the LHC, where tremendous data rates necessitate processing solutions with exceptionally low latency. Traditional approaches using ASICs and commercial CPUs are limited by either inflexibility or inadequate speed. FPGAs, however, offer a reprogrammable platform capable of pipelined processing, balancing properties of both hardware acceleration and algorithmic flexibility.

Implementation Approach

The authors present a case paper utilizing neural networks for jet substructure classification, which showcases how FPGAs can be leveraged to deliver results within the stringent latency requirements characteristic of LHC environments, approximately 100 ns. A companion tool, based on High-Level Synthesis (HLS), simplifies the translation of neural network models into FPGA firmware, providing a tangible pathway for deploying neural networks in low-latency FPGA systems.

The implementation of neural networks in FPGAs involves several considerations regarding resource usage and algorithm efficiency. The paper discusses strategies involving network compression, quantization of calculations to fixed-point arithmetic, and optimization of computation through parallelization (or reuse of computational resources). These optimizations are assessed in terms of their impact on FPGA resource utilization, model latency, and initiation intervals.

Results and Implications

The case paper demonstrates the feasibility of deploying compressed, quantized neural networks on Xilinx Kintex Ultrascale FPGAs, achieving significant reductions in DSP usage, with practical latencies demonstrating compatibility with LHC trigger stage constraints. The neural network model is designed for classifying jets into categories such as quark, gluon, and bosons ($W$, $Z$, top quark), using inputs that capture jet substructure properties. The research illustrates a compact implementation using approximately 10% of a Kintex Ultrascale's DSPs, dependent on the reuse factor settings and precision of fixed-point operations. The observed latency, less than 150 ns, sustains the real-time performance required for LHC applications.

Future Directions

The paper posits that the capability to perform neural network inference on FPGAs not only enhances data processing at the LHC but could have broad implications for fast, on-detector processing in various experimental contexts featuring high rate and complex data streams. The tool hls4ml developed as part of the research enables rapid prototyping and experimentation with machine learning workflows, potentially democratizing access to FPGA deployment for physicists, and reducing dependency on expert firmware engineers.

Going forward, advancements in FPGA technology and further development of adaptive algorithms could enable even more sophisticated tasks, expanding the potential applications of FPGAs in both particle physics and beyond. Such developments pave the way for implementing more complex neural network architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to meet a range of data processing challenges, while maintaining stringent latency and efficiency criteria.