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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))

   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, themore »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.« less
2. 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 studyingmore »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.« less
3. 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)

   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 aremore »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.« less
4. NetML: An NFV Platform with Efficient Support for Machine Learning Applications

   https://doi.org/10.1109/NETSOFT.2019.8806698  

   Dhakal, Aditya ; Ramakrishnan, K. K. ( June 2019 , 2019 IEEE Conference on Network Softwarization (NetSoft))

   Real-time applications such as autonomous and connected cars, surveillance, and online learning applications have to train on streaming data. They require low-latency, high throughput machine learning (ML) functions resident in the network and in the cloud to perform learning and inference. NFV on edge cloud platforms can provide support for these applications by having heterogeneous computing including GPUs and other accelerators to offload ML-related computation. GPUs provide the necessary speedup for performing learning and inference to meet the needs of these latency sensitive real-time applications. Supporting ML inference and learning efficiently for streaming data in NFV platforms has several challenges.more »In this paper, we present a framework, NetML, that runs existing ML applications on an heterogeneous NFV platform that includes both CPUs and GPUs. NetML efficiently transfers the appropriate packet payload to the GPU, minimizing overheads, avoiding locks, and avoiding CPU-based data copies. Additionally, NetML minimizes latency by maximizing overlap between the data movement and GPU computation. We evaluate the efficiency of our approach for training and inference using popular object detection algorithms on our platform. NetML reduces the latency for inferring images by more than 20% and increases the training throughput by 30% while reducing CPU utilization compared to other state-of-the-art alternatives.« less
5. Research Opportunities in Heterogeneous Computing for Machine Learning

   https://doi.org/10.1109/HPCS.2018.00094  

   Lam, Herman ; Ojika, David ( July 2018 , International Workshop on High Performance and Dynamic Reconfigurable Systems and Networks (DRSN))

   In recent times, AI and deep learning have witnessed explosive growth in almost every subject involving data. Complex data analyses problems that took prolonged periods, or required laborious manual effort, are now being tackled through AI and deep-learning techniques with unprecedented accuracy. Machine learning (ML) using Convolutional Neural Networks (CNNs) has shown great promise for such applications. However, traditional CPU-based sequential computing no longer can meet the requirements of mission-critical applications which are compute-intensive and require low latency. Heterogeneous computing (HGC), with CPUs integrated with accelerators such as GPUs and FPGAs, offers unique capabilities to accelerate CNNs. In this presentation,more »we will focus on using FPGA-based reconfigurable computing to accelerate various aspects of CNN. We will begin with the current state of the art in using FPGAs for CNN acceleration, followed by the related R&D activities (outlined below) in the SHREC* Center at the University of Florida, based on which we will discuss the opportunities in heterogeneous computing for machine learning.« less