Fast Machine Learning Survives Intense Radiation Tests

Researchers are addressing the challenge of deploying machine learning in high-radiation environments, crucial for future high-energy physics experiments. Katya Govorkova, Julian Garcia Pardinas, and Vladimir Loncar, working with colleagues at Massachusetts Institute of Technology (MIT), European Organization for Nuclear Research (CERN), and University of Milano-Bicocca (Italy), demonstrate a viable, ultra-fast machine learning application on radiation-hard FPGAs using the hls4ml framework. This work, exemplified by a test case utilising the PicoCal calorimeter planned for the LHCb Upgrade II experiment, details the development of a lightweight autoencoder for data compression, a hardware-aware quantization strategy minimising performance loss, and a novel backend for hls4ml enabling automatic translation of machine learning models onto Microchip PolarFire FPGAs. Achieving a latency of 25ns, this research represents a significant step towards broader adoption of on-detector machine learning in challenging radiation environments.

Scientists are bringing the power of machine learning to bear on the extreme conditions found within particle physics experiments. The ability to process data rapidly and reliably, even when bombarded by radiation, is essential for future discoveries. This work demonstrates a pathway to achieving that, opening up new possibilities for real-time analysis at the Large Hadron Collider and beyond.

Scientists are pioneering a new approach to data processing for high-energy physics, demonstrating the first viable, ultra-fast machine learning (ML) application designed to withstand intense radiation. Initially, a lightweight autoencoder was developed to compress 32-sample timing readouts, characteristic of the PicoCal, into a two-dimensional latent space.

This compression is vital, as current estimates require reducing an initial sample of 32 inputs to a maximum of two numbers of similar bit size on the detector to maintain tenable data rates. Achieving this compression in a high-radiation environment demands specialised hardware and software. This extension to hls4ml represents a considerable advancement, opening the door for wider adoption of ML on FPGAs in environments where radiation is a major concern. By successfully combining a tailored ML algorithm with a radiation-hardened hardware platform and a streamlined development toolchain, this work establishes a pathway for real-time data processing at the frontiers of particle physics. The ability to perform this compression on-detector, rather than transmitting vast amounts of raw data, promises to alleviate bandwidth limitations and unlock the full potential of future experiments.

Ultra-low latency and dimensionality reduction for high-energy physics data

A latency of 25 nanoseconds was achieved through the synthesis of the autoencoder on a Microchip PolarFire FPGA, a critical parameter for real-time applications within high-energy physics experiments. The design successfully meets the application’s strict performance requirements, enabling ultra-fast data processing. The autoencoder demonstrably reduced a 32-sample timing readout into a two-dimensional latent space, maintaining key physics information and allowing for efficient data handling without sacrificing crucial details needed for analysis.

By learning a compressed representation of the data, the model captures the most salient features of the pulse shapes. Careful optimisation of the model for hardware implementation was necessary to achieve this performance. The research team employed a hardware-aware quantization strategy, successfully reducing the model’s weights to 10-bit precision with minimal performance loss.

This reduction in bit-width lowers resource utilisation on the FPGA, allowing for more complex models or increased throughput. A significant hurdle to wider adoption of machine learning on radiation-hard FPGAs was addressed through the development of a new backend for the hls4ml library. This autoencoder reduces a 32-sample timing readout into a two-dimensional latent space, preserving essential information for subsequent physics reconstruction. Simulations validated the model’s effectiveness in this compression task, demonstrating its ability to retain critical data characteristics.

Achieving practical deployment demanded more than just a functional algorithm. A systematic hardware-aware quantization strategy was implemented to minimise the model’s computational demands, reducing the precision of the model’s weights to ultimately achieve a 10-bit representation with minimal impact on physics reconstruction performance. Decreasing the bit-width lowers the resource requirements for implementation, making it more suitable for deployment on resource-constrained hardware while ensuring the model remains accurate enough for its intended purpose.

A significant obstacle to on-detector machine learning adoption existed: the standard high-energy physics machine learning synthesis tool, hls4ml, lacked support for radiation-hard FPGAs. The autoencoder was then synthesised onto a target PolarFire FPGA, revealing a latency of 25ns could be achieved. This performance is a direct result of the model compression and the efficient HLS implementation facilitated by the new backend.

Furthermore, resource utilisation was low enough to allow placement within the FPGA’s inherently protected logic, a critical requirement for operation in high-radiation environments. This entire process represents the first end-to-end demonstration of a viable, ultra-fast machine learning application on a radiation-hard FPGA for a future high-energy physics experiment.

Deploying machine learning for real-time data analysis in high-radiation environments

For years, the promise of machine learning in particle physics has outstripped the practicalities of deployment. While algorithms excel at sifting through immense datasets, the environments where these datasets originate, inside colossal experiments like those at the Large Hadron Collider, present unique challenges. Intense radiation, for example, quickly degrades standard electronics, demanding specialised, and expensive, hardware.

This work doesn’t simply present another algorithm; it demonstrates a pathway to actually using machine learning where it matters most, within the heart of a high-energy physics detector. Building systems that withstand bombardment from subatomic particles is only half the battle. The computational demands of real-time data analysis require custom hardware, and the standard tools for translating machine learning models into these systems have historically lacked support for the radiation-hardened field-programmable gate arrays (FPGAs) favoured by experimentalists.

A new back-end for the hls4ml library addresses this directly, offering an automated route from algorithm to functioning detector component. This is a subtle but vital shift, lowering the barrier to entry for wider adoption. Limitations remain. The demonstrated compression and processing speeds, while impressive, represent a single test case, the PicoCal calorimeter, and scaling this to the full complexity of a modern detector will undoubtedly present further hurdles. The demonstrated compression and processing speeds, while impressive, represent a single test case, the PicoCal calorimeter, and scaling this to the full complexity of a modern detector will undoubtedly present further hurdles.

Furthermore, the long-term effects of radiation on the compressed models themselves require investigation; will their performance degrade over time, necessitating retraining or recalibration. Beyond this, the broader field will likely see a move towards even more efficient algorithms and hardware architectures. However, this work provides a solid foundation, suggesting that the era of on-detector machine learning is no longer a distant prospect but a tangible possibility.