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Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition

Haoyi Jia, Abhilasha Dave, Julia Gonski, Ryan Herbst

TL;DR

An analysis of neural network inference efficiency is presented, focusing on the application of collider trigger algorithms in field programmable gate arrays (FPGAs) and trade-offs are evaluated between two frameworks, the SNL and hls4ml, offering valuable insights for optimizing real-time neural network deployments at colliders.

Abstract

To fully exploit the physics potential of current and future high energy particle colliders, machine learning (ML) can be implemented in detector electronics for intelligent data processing and acquisition. The implementation of ML in real-time at colliders requires very low latencies that are unachievable with a software-based approach, requiring optimization and synthesis of ML algorithms for deployment on hardware. An analysis of neural network inference efficiency is presented, focusing on the application of collider trigger algorithms in field programmable gate arrays (FPGAs). Trade-offs are evaluated between two frameworks, the SLAC Neural Network Library (SNL) and hls4ml, in terms of resources and latency for different model sizes. Results highlight the strengths and limitations of each approach, offering valuable insights for optimizing real-time neural network deployments at colliders. This work aims to guide researchers and engineers in selecting the most suitable hardware and software configurations for real-time, resource-constrained environments.

Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition

TL;DR

An analysis of neural network inference efficiency is presented, focusing on the application of collider trigger algorithms in field programmable gate arrays (FPGAs) and trade-offs are evaluated between two frameworks, the SNL and hls4ml, offering valuable insights for optimizing real-time neural network deployments at colliders.

Abstract

To fully exploit the physics potential of current and future high energy particle colliders, machine learning (ML) can be implemented in detector electronics for intelligent data processing and acquisition. The implementation of ML in real-time at colliders requires very low latencies that are unachievable with a software-based approach, requiring optimization and synthesis of ML algorithms for deployment on hardware. An analysis of neural network inference efficiency is presented, focusing on the application of collider trigger algorithms in field programmable gate arrays (FPGAs). Trade-offs are evaluated between two frameworks, the SLAC Neural Network Library (SNL) and hls4ml, in terms of resources and latency for different model sizes. Results highlight the strengths and limitations of each approach, offering valuable insights for optimizing real-time neural network deployments at colliders. This work aims to guide researchers and engineers in selecting the most suitable hardware and software configurations for real-time, resource-constrained environments.

Paper Structure

This paper contains 10 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Data Input Rate and Latency Implications
  3. High-Level Synthesis and Variability in FPGA Implementations
  4. Managing Complexity in Model Size and Architecture
  5. Synthesis Frameworks
  6. SLAC Neural network Library (SNL)
  7. hls4ml
  8. Models & Dataset
  9. Synthesis Results
  10. Conclusions

Figures (5)

  • Figure 1: High-level design flow for the SLAC Neural network Library (SNL).
  • Figure 2: High-level design flow for hls4mlfastml_hls4ml.
  • Figure 3: Architecture diagram for the smallest VAE encoder benchmark model (Model 1). Two other models, 2 and 3, share the same structure but have a number of nodes per layer that is a factor of 2 and 4 larger, respectively. Only the encoder stage is implemented on the FPGA, as indicated.
  • Figure 4: Plots comparing the usage of BRAM, DSPs, LUTs, FFs, and latency for the 3 benchmark models with ap_fixed<32,16> quantization.
  • Figure 5: Plots comparing the usage of BRAM, DSPs, LUTs, FFs, and latency for the 3 benchmark models with ap_fixed<16,8> quantization.