923 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
Investigation, Development, and Evaluation of Performance Proving for Fault-tolerant Computers
A number of methodologies for verifying systems and computer based tools that assist users in verifying their systems were developed. These tools were applied to verify in part the SIFT ultrareliable aircraft computer. Topics covered included: STP theorem prover; design verification of SIFT; high level language code verification; assembly language level verification; numerical algorithm verification; verification of flight control programs; and verification of hardware logic
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow
For tasks like code synthesis from natural language, code retrieval, and code
summarization, data-driven models have shown great promise. However, creating
these models require parallel data between natural language (NL) and code with
fine-grained alignments. Stack Overflow (SO) is a promising source to create
such a data set: the questions are diverse and most of them have corresponding
answers with high-quality code snippets. However, existing heuristic methods
(e.g., pairing the title of a post with the code in the accepted answer) are
limited both in their coverage and the correctness of the NL-code pairs
obtained. In this paper, we propose a novel method to mine high-quality aligned
data from SO using two sets of features: hand-crafted features considering the
structure of the extracted snippets, and correspondence features obtained by
training a probabilistic model to capture the correlation between NL and code
using neural networks. These features are fed into a classifier that determines
the quality of mined NL-code pairs. Experiments using Python and Java as test
beds show that the proposed method greatly expands coverage and accuracy over
existing mining methods, even when using only a small number of labeled
examples. Further, we find that reasonable results are achieved even when
training the classifier on one language and testing on another, showing promise
for scaling NL-code mining to a wide variety of programming languages beyond
those for which we are able to annotate data.Comment: MSR '1
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