EdgeAI: Machine learning via direct attached accelerator for streaming data processing at high shot rate x-ray free-electron lasers - Kraus, Mike; Layad, Naoufal; Liu, Zhengchun; Coffee, Ryan (Frontiers in Physics, 2022)
We present a case for low batch-size inference with potential for adaptive training of a lean encoder model. %at the computational edge. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific user facilities such as the Linac Coherent Light Source-II x-ray Free Electron Laser. We discuss how a low latency inference model operating at the data acquisition edge can capitalize on the naturally stochastic nature of such sources. We simulate the method of attosecond angular streaking to produce representative results whereby simulated input data reproduces high resolution ground truth probability distributions. By minimizing the mean-squared-error between the decoded output of the latent representation and the ground truth distributions, we ensure that the encoding layers and resulting latent representation maintains full fidelity for any down-stream task, be it classification or regression. We present throughput results for data parallel inference of various batch sizes, some with throughput exceeding 100k images per second. We also show in situ training below 10 seconds per epoch for the full encoder-decoder model as would be relevant for streaming and adaptive real-time data production at our nation's scientific light sources.