Building Real Time Edge Machine Learning Systems for High Data Rate Acquisition

Over the past decade, a developments in radiation and photonic detectors has significantly improved their resolution, pixel density, sensitivity, and sampling rate. The increase in sampling rate corresponds to a considerable increase in generated data, the movement and storage of which requires a large number of storage units with very high bandwidth interconnects to the sensors themselves. The paradigm of edge computing, however, proposes to move the data processing closer to the source, the edge, rather than moving data to processing. Still, the computation resources are limited at the edge and it is necessary to use lean and robust algorithms. Machine learning (ML) is commonly used to identify patterns and relationships in minimally processed data. EdgeML is a combination of ML and edge computing that leverages a combination of benefits from ML algorithms and edge computing. In this paper, we demonstrate a high-speed and configurable ML model in a fully customizable EdgeML flow. Our demonstration focuses on an angular streaking detector developed for the LCLS-II project known as the CookieBox. The flow starts by emulating the CookieBox, digitizing the signals, and passing them to an optimized ML model on the FPGA. By using our ML implementation in this flow, we are able to achieve a 2.7 μs of inference latency. This flow can also be configured for other instrumentation applications that require low-latency solutions.