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Adapting scientific streaming inference workflows for a deterministic tensor processing unit
DescriptionThe realization of real-time data processing near X-ray detectors presents ongoing challenges due to long ASIC development cycles and the limited computational capacity of near-detector FPGAs. We propose a hybrid solution that streams data directly to a deterministic tensor processing unit (i.e., Groq AI accelerator), enabling low-latency, high-throughput inference. This paper describes the system architecture, supporting software stack, and performance projections, demonstrating the advantages of this hybrid platform for future X-ray imaging systems. This integration shows promise for advancing real-time edge computing and enabling intelligent control in photon science experiments. A single inference on a 128 × 128 image, including image transfer time, completes in 156.06 𝜇s, enabling approximately 6.4 kHz processing with the edgePtychoNN model and improving experimental-in-the-loop computing. Using this system, we achieve a 3.6× speedup over previous systems, highlighting the potential of this approach
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