In many experiment-driven scientific domains, such as high-energy physics, material science, and cosmology, high data rate experiments impose hard constraints on data acquisition systems: collected data must either be indiscriminately stored for post-processing and analysis, thereby necessitating large storage capacity, or accurately filtered in real-time, thereby necessitating low-latency processing. Deep neural networks, effective in other filtering tasks, have not been widely employed in such data acquisition systems, 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-level representations of deep neural networks to low-level representations, suitable for deployment to near-sensor devices such as field-programmable gate arrays. We evaluate OpenHLS on various workloads and present a case-study implementation of a deep neural network for Bragg peak detection in the context of high-energy diffraction microscopy. We show OpenHLS is able to produce an implementation of the network with a throughput 4.8 $\mu$s/sample, which is approximately a 4$\times$ improvement over the existing implementation
翻译:在许多实验驱动的科学领域,如高能物理学、材料科学和宇宙学,高数据率实验对数据获取系统施加了严格的限制:收集的数据必须不加区分地储存,以供后处理和分析之用,从而需要大量的储存能力,或准确实时过滤,从而需要低时空处理;由于设计和部署方面的困难,在这类数据获取系统中没有广泛使用在其它过滤任务中有效的深神经网络;我们提出了一个开放源、轻量、编译框架,没有任何专有依赖性;基于高层次合成技术的开放HLS,用于将深神经网络的高层代表转变为低层次代表,适合于部署近感官装置,例如外地可规划的门阵列;我们评估了不同工作量的开放HLS,并介绍了在高能分解显微镜中进行布拉格峰测深神经网络的案例研究实施情况;我们显示,OpHLS能够以4.8美元/mu=超时实施网络,这大约是4美元/超时的改进。</s>