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1. FPGAs-as-a-Service Toolkit (FaaST)

   https://doi.org/10.1109/H2RC51942.2020.00010  

   Rankin, Dylan; Krupa, Jeffrey; Harris, Philip; Flechas, Maria Acosta; Holzman, Burt; Klijnsma, Thomas; Pedro, Kevin; Tran, Nhan; Hauck, Scott; Hsu, Shih-Chieh; et al (November 2020, 2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC))

   null (Ed.)

   Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs are an extremely promising option as well. A series of workflows are developed to establish the performance capabilities of FPGAs as a service. Multiple different devices and a range of algorithms for use in high energy physics are studied. For a small, dense network, the throughput can be improved by an order of magnitude with respect to GPUs as a service. For large convolutional networks, the throughput is found to be comparable to GPUs as a service. This work represents the first open-source FPGAs-as-a-service toolkit. 

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2. HeteroBench: Multi-kernel Benchmarks for Heterogeneous Systems

   https://doi.org/10.1145/3676151.3719366  

   Tian, Hongzheng; Mishra, Alok; Chen, Zhiheng; Hong_Enriquez, Rolando P; Milojicic, Dejan; Frachtenberg, Eitan; Huang, Sitao (May 2025, ACM)

   The end of Moore’s Law and Dennard scaling has driven the proliferation of heterogeneous systems with accelerators, including CPUs, GPUs, and FPGAs, each with distinct architectures, compilers, and programming environments. GPUs excel at massively parallel processing for tasks like deep learning training and graphics rendering, while FPGAs offer hardware-level flexibility and energy efficiency for low-latency, high-throughput applications. In contrast, CPUs, while general-purpose, often fall short in high-parallelism or power-constrained applications. This architectural diversity makes it challenging to compare these accelerators effectively, leading to uncertainty in selecting optimal hardware and software tools for specific applications. To address this challenge, we introduce HeteroBench, a versatile benchmark suite for heterogeneous systems. HeteroBench allows users to evaluate multi-compute kernel applications across various accelerators, including CPUs, GPUs (from NVIDIA, AMD, Intel), and FPGAs (AMD), supporting programming environments of Python, Numba-accelerated Python, serial C++, OpenMP (both CPUs and GPUs), OpenACC and CUDA for GPUs, and Vitis HLS for FPGAs. This setup enables users to assign kernels to suitable hardware platforms, ensuring comprehensive device comparisons. What makes HeteroBench unique is its vendor-agnostic, cross-platform approach, spanning diverse domains such as image processing, machine learning, numerical computation, and physical simulation, ensuring deeper insights for HPC optimization. Extensive testing across multiple systems provides practical reference points for HPC practitioners, simplifying hardware selection and performance tuning for both developers and end-users alike. This suite may assist to make more informed decision on AI/ML deployment and HPC development, making it an invaluable resource for advancing academic research and industrial applications. 

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3. Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs

   https://doi.org/10.1007/978-3-030-34356-9_44  

   Jiang, C.; Ojika, D.; Vallecorsa, S.; Kurth, T.; Prabhat, P.; Patel, B.; Lam, H. (November 2019, 24th International Conference on Computing in High Energy and Nuclear Physics (CHEP19))

   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. 

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4. Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA

   https://doi.org/10.1051/epjconf/202024509014  

   C. Jiang, D. Ojika (November 2020, EPJ web of conferences)

   null (Ed.)

   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 without special instructions 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 GPUs, FPGAs, and other science-targeted accelerators, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC1at the University of Florida, CERN Openlab, NERSC2at Lawrence Berkeley National Lab, 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 2.46x to 9.59x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU. 

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5. Survey of Hardware Acceleration of Genomic Analysis

   Liu, Zhuren; Zhang, Shouzhe; Zhao, Hui (December 2024, IEEE)

   As the Next-Generation Sequencing (NGS) techniques need to process enormous amounts of data, cost-efficientfand high-throughput computational analysis is essential in genomicsfstudy. Conventional computing platforms face great challenges to meet these demands due to their limited processing speed and scalability. Hardware accelerators, such as Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), offer transformative solutions to these computational challenges. This paper provides a state-of-the-art review of the roles of hardware accelerators in genomic analysis.We performed a comprehensive and in-depth analysis of cutting-edge genomics hardware accelerators, such as GPUs, FPGAs, and ASICs, in the context of the specific algorithms they aim to enhance. Besides reviewing opportunities in hardware genome acceleration, we also provide insights into the challenges regarding processing speed, cost efficiency, and scalability. 

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