FPGA-Based Real-Time Waveform Classification
A new hardware-optimized AI model processes sensor data with zero dead time, slashing data transmission needs.
A research team including Ilja Bekman and Alperen Aksoy has published a novel method for real-time waveform classification using FPGAs (Field-Programmable Gate Arrays). The system is designed for self-triggered readout of silicon photomultiplier (SiPM) sum signals in particle physics experiments, where it acts as an intelligent filter to aid a simple threshold trigger. By classifying calorimetric particle hit information online at an early stage, the technology significantly reduces the volume of data that needs to be transmitted from the detector edge, addressing a major bottleneck in high-energy physics data acquisition systems that typically rely on FPGAs for initial processing.
The technical core of the work involves implementing look-up-table-based binary multi-layer neural networks directly in hardware. The researchers tackled challenges related to the neural network's resource footprint, hardware layout, and training, demonstrating that these compact structures can be effectively trained using a genetic algorithm. Crucially, the achieved inference latency is compatible with dead-time-free processing, meaning the system can analyze incoming sensor data continuously without missing events. This represents a specialized but powerful application of edge AI, moving complex classification tasks directly to the sensor interface to enable more efficient and scalable scientific instrumentation.
- Uses look-up-table-based binary neural networks implemented on FPGAs for minimal hardware footprint
- Trained with a genetic algorithm to achieve latency compatible with dead-time-free online processing
- Designed to filter SiPM sensor data at the edge, reducing transmitted data volume in particle physics experiments
Why It Matters
Enables more efficient, real-time data filtering at the sensor level for large-scale physics experiments and edge AI applications.