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In many experiment-driven scientific domains, such as high-energy physics,material science, and cosmology, high data rate experiments impose hardconstraints on data acquisition systems: collected data must either beindiscriminately stored for post-processing and analysis, thereby necessitatinglarge storage capacity, or accurately filtered in real-time, therebynecessitating low-latency processing. Deep neural networks, effective in otherfiltering tasks, have not been widely employed in such data acquisitionsystems, due to design and deployment difficulties. We present an open source,lightweight, compiler framework, without any proprietary dependencies, OpenHLS,based on high-level synthesis techniques, for translating high-levelrepresentations of deep neural networks to low-level representations, suitablefor deployment to near-sensor devices such as field-programmable gate arrays.We evaluate OpenHLS on various workloads and present a case-studyimplementation of a deep neural network for Bragg peak detection in the contextof high-energy diffraction microscopy. We show OpenHLS is able to produce animplementation of the network with a throughput 4.8 $\mu$s/sample, which isapproximately a 4$\times$ improvement over the existing implementation

OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science