Researchers are taking on the challenge of deploying machine learning in high-radiation environments essential for future high-energy physics experiments. Katya Govorkova, Julian Garcia Pardinas, and Vladimir Loncar collaborate with colleagues from the Massachusetts Institute of Technology (MIT), the European Organization for Nuclear Research (CERN), and the University of Milano-Bicocca (Italy) to demonstrate a viable ultrafast machine learning application on a radiation-hardened FPGA using the hls4ml framework. This work is exemplified by a test case utilizing the PicoCal calorimeter planned for the LHCb Upgrade II experiment and details the development of a lightweight autoencoder for data compression, a hardware-aware quantization strategy to minimize performance loss, and a new backend for hls4ml that enables automatic translation of machine learning models to Microchip PolarFire FPGAs. Achieving a delay of 25 ns, this study represents an important step toward widespread adoption of on-detector machine learning in difficult radiation environments.
Scientists are bringing the power of machine learning to the extreme conditions found in particle physics experiments. The ability to process data quickly and reliably, even when exposed to radiation, is essential for future discoveries. This research provides a path to achieving that goal and opens up new possibilities for real-time analysis of the Large Hadron Collider and beyond.
Scientists are pioneering a new approach to data processing for high-energy physics, demonstrating the first viable ultrafast machine learning (ML) application designed to withstand intense radiation. Initially, a lightweight autoencoder was developed to compress PicoCal’s signature 32-sample timing readout into a two-dimensional latent space.
This compression is critical because current estimates require reducing the initial samples of 32 inputs to at most two numbers of similar bit size on the detector in order to maintain a sustainable data rate. Achieving this compression in high radiation environments requires specialized hardware and software. This enhancement to hls4ml represents a significant advance and opens the door for broader adoption of ML on FPGAs in environments where radiation is a major concern. This research establishes a pathway for real-time data processing at the forefront of particle physics by successfully combining customized ML algorithms with a radiation-hardened hardware platform and a streamlined development toolchain. The ability to perform this compression on the detector, rather than transmitting huge amounts of raw data, is expected to alleviate bandwidth limitations and maximize the potential of future experiments.
Ultra-low latency and dimensionality reduction for high-energy physical data
A latency of 25 nanoseconds was achieved by synthesizing an autoencoder on a Microchip PolarFire FPGA, a critical parameter for real-time applications in high-energy physics experiments. This design adequately meets the demanding performance requirements of applications and enables ultra-fast data processing. The autoencoder was demonstrated to reduce the 32-sample timing readout to a two-dimensional latent space, preserve important physical information, and enable efficient data processing without sacrificing critical details needed for analysis.
By learning a compressed representation of the data, the model captures the most salient features of the pulse shape. Achieving this performance required careful optimization of the model for hardware implementation. Using a hardware-aware quantization strategy, the researchers were able to reduce model weights to 10-bit precision with minimal performance loss.
This reduction in bit width reduces resource utilization on the FPGA, enabling more complex models and increased throughput. A major hurdle to widespread adoption of machine learning in radiation-hardened FPGAs has been resolved through the development of a new backend for the hls4ml library. This autoencoder reduces the 32-sample timing readout to a two-dimensional latent space and stores the information needed for subsequent physical reconstruction. Simulations verified the effectiveness of the model in this compression task and demonstrated its ability to preserve important data characteristics.
Achieving practical deployment required more than just a functional algorithm. To minimize the computational demands of the model, a systematic hardware-aware quantization strategy was implemented to reduce the precision of the model weights, ultimately achieving a 10-bit representation with minimal impact on physical reconstruction performance. A smaller bit width requires fewer resources to implement, making it more suitable for deployment on resource-constrained hardware, while ensuring that the model remains accurate enough for its intended purpose.
There have been significant obstacles to implementing machine learning on detectors. The standard high-energy physics machine learning synthesis tool hls4ml lacked support for radiation-hardened FPGAs. Next, an autoencoder was synthesized on the target PolarFire FPGA, and it was shown that a latency of 25ns could be achieved. This performance is a direct result of model compression and an efficient HLS implementation facilitated by a new backend.
Additionally, resource utilization is low enough that it can be placed within the inherently protected logic of the FPGA, which is a key requirement for operation in high radiation environments. This entire process represents the first end-to-end demonstration of ultra-fast machine learning applications that can be run on radiation-hardened FPGAs for future high-energy physics experiments.
Deploying machine learning for real-time data analysis in high radiation environments
For many years, the promise of machine learning in particle physics has outstripped the reality of its implementation. Algorithms are great at sifting through huge datasets, but the environment in which these datasets are generated within large experiments like the Large Hadron Collider presents unique challenges. For example, powerful radiation rapidly degrades standard electronic equipment and requires specialized and expensive hardware.
This research does more than just present another algorithm. actually shows you the way using Machine learning at the most critical location in the heart of high-energy physical detectors. Building systems that can withstand attacks from subatomic particles is only half the battle. The computational demands of real-time data analysis require custom hardware, but standard tools for converting machine learning models into these systems have historically lacked support for the radiation-hardened field programmable gate arrays (FPGAs) favored by experimenters.
The new backend in the hls4ml library directly addresses this and provides an automated route from the algorithm to the functioning detector component. This is a subtle but important change, lowering the barrier to entry for broader adoption. Restrictions remain. Although the demonstrated compression and processing speeds are impressive, they represent a single test case, the PicoCal calorimeter, and there are undoubtedly further hurdles to extend this to the full complexity of modern detectors. Although the demonstrated compression and processing speeds are impressive, they represent a single test case, the PicoCal calorimeter, and there are undoubtedly further hurdles to extend this to the full complexity of modern detectors.
Additionally, the long-term effects of radiation on the compression model itself require investigation. Whether performance deteriorates over time and requires retraining or reconditioning. Beyond this, the broader field will see a shift towards more efficient algorithms and hardware architectures. However, this study provides a solid foundation and suggests that the era of machine learning on detectors is no longer a distant prospect, but a concrete possibility.
