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AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. However, traditional CPU-based sequential computing can no longer meet the requirements of mission-critical applications, which are compute-intensive and require low latency and high throughput. Heterogeneous computing (HGC), with CPUs integrated with accelerators such as GPUs and FPGAs, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC\inst{1} at the University of Florida, NERSC\inst{2} at Lawrence Berkeley National Lab, CERN Openlab, Dell EMC, and Intel are studying the application of heterogeneous computing (HGC) to scientific problems using DNN models. This paper focuses on the use of FPGAs to accelerate the inferencing stage of the HGC workflow. We present case studies and results in inferencing state-of-the-art DNN models for scientific data analysis, using Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA. Using the Intel Deep Learning Acceleration (DLA) development suite to optimize existing FPGA primitives and develop new ones, we were able accelerate the scientific DNN models under study with a speedup from 3x to 6x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU. 

SCAIGATE is an ambitious project to design the first AI-centric science gateway based on field-programmable gate arrays (FPGAs). The goal is to democratize access to FPGAs and AI in scientific computing and related applications. When completed, the project will enable the large-scale deployment and use of machine learning models on AI-centric FPGA platforms, allowing increased performance-efficiency, reduced development effort, and customization at unprecedented scale, all while simplifying ease-of-use in science domains which were previously AI-lagging. SCAIGATE was an incubation project at the Science Gateway Community Institute (SGCI) bootcamp held in Austin, Texas in 2018. 

Field-programmable gate arrays (FPGAs) have largely been used in communication and high-performance computing, and given the recent advances in big data, machine learning and emerging trends in cloud computing (e.g., serverless [1]), FPGAs are increasingly being introduced into these domains (e.g., Microsoft’s datacenters [2] and Amazon Web Services [3]). To address these domains’ processing needs, recent research has focused on using FPGAs to accelerate workloads, ranging from analytics and machine learning to databases and network function virtualization. In this paper, we present a high-performance FPGA-as-a-microservice (FaaM) architecture for the cloud. We discuss some of the technical challenges and propose several solutions for efficiently integrating FPGAs into virtualized environments. Our case study deploying a multi-threaded, multi-user compression as a microservice using FaaM indicates that microservices-based FPGA acceleration can sustain high-performance as compared to a straightforward CPU implementation with minimal to no communication overhead despite the hardware abstraction.