322 research outputs found
FPGA-accelerated machine learning inference as a service for particle physics computing
New heterogeneous computing paradigms on dedicated hardware with increased
parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting
solutions with large potential gains. The growing applications of machine
learning algorithms in particle physics for simulation, reconstruction, and
analysis are naturally deployed on such platforms. We demonstrate that the
acceleration of machine learning inference as a web service represents a
heterogeneous computing solution for particle physics experiments that
potentially requires minimal modification to the current computing model. As
examples, we retrain the ResNet-50 convolutional neural network to demonstrate
state-of-the-art performance for top quark jet tagging at the LHC and apply a
ResNet-50 model with transfer learning for neutrino event classification. Using
Project Brainwave by Microsoft to accelerate the ResNet-50 image classification
model, we achieve average inference times of 60 (10) milliseconds with our
experimental physics software framework using Brainwave as a cloud (edge or
on-premises) service, representing an improvement by a factor of approximately
30 (175) in model inference latency over traditional CPU inference in current
experimental hardware. A single FPGA service accessed by many CPUs achieves a
throughput of 600--700 inferences per second using an image batch of one,
comparable to large batch-size GPU throughput and significantly better than
small batch-size GPU throughput. Deployed as an edge or cloud service for the
particle physics computing model, coprocessor accelerators can have a higher
duty cycle and are potentially much more cost-effective.Comment: 16 pages, 14 figures, 2 table
Programmable photonics : an opportunity for an accessible large-volume PIC ecosystem
We look at the opportunities presented by the new concepts of generic programmable photonic integrated circuits (PIC) to deploy photonics on a larger scale. Programmable PICs consist of waveguide meshes of tunable couplers and phase shifters that can be reconfigured in software to define diverse functions and arbitrary connectivity between the input and output ports. Off-the-shelf programmable PICs can dramatically shorten the development time and deployment costs of new photonic products, as they bypass the design-fabrication cycle of a custom PIC. These chips, which actually consist of an entire technology stack of photonics, electronics packaging and software, can potentially be manufactured cheaper and in larger volumes than application-specific PICs. We look into the technology requirements of these generic programmable PICs and discuss the economy of scale. Finally, we make a qualitative analysis of the possible application spaces where generic programmable PICs can play an enabling role, especially to companies who do not have an in-depth background in PIC technology
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
Acta Universitatis Sapientiae - Electrical and Mechanical Engineering
Series Electrical and Mechanical Engineering publishes original papers and surveys in various fields of Electrical and Mechanical Engineering
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