Related papers: Data Acquisition System with Shared Memory Network
The data acquisition console is an important component of the EAST data acquisition system which provides unified data acquisition and long-term data storage for diagnostics. The data acquisition console is used to manage the data…
In applications with segmented high purity Ge detectors or other detector arrays with tens or hundreds of channels, where the high development cost and limited flexibility of application specific integrated circuits outweigh their benefits…
Cloud-supported Internet of Things (Cloud-IoT) has been broadly deployed in smart grid systems. The IoT front-ends are responsible for data acquisition and status supervision, while the substantial amount of data is stored and managed in…
Data Acquisition and Control Systems used in high energy physics experiments, such as those which will take place in the Large Hadron Collider (LHC) at CERN, require the specification of data formats and transmission protocols as well as…
Data processing frameworks such as Apache Beam and Apache Spark are used for a wide range of applications, from logs analysis to data preparation for DNN training. It is thus unsurprising that there has been a large amount of work on…
Recently many effective attention modules are proposed to boot the model performance by exploiting the internal information of convolutional neural networks in computer vision. In general, many previous works ignore considering the design…
The Intense Pulsed Neutron Source has been an operating user facility for more than 20 years. Development of an upgrade for the data acquisition system has been in progress for some time now. Now that the initial installation on the test…
For several decades, the CPU has been the standard model to use in the majority of computing. While the CPU does excel in some areas, heterogeneous computing, such as reconfigurable hardware, is showing increasing potential in areas like…
High-end ARM processors are emerging in data centers and HPC systems, posing as a strong contender to x86 machines. Memory-centric profiling is an important approach for dissecting an application's bottlenecks on memory access and guiding…
Read-optimized columnar databases use differential updates to handle writes by maintaining a separate write-optimized delta partition which is periodically merged with the read-optimized and compressed main partition. This merge process…
CoWrangler is a data-wrangling recommender system designed to streamline data processing tasks. Recognizing that data processing is often time-consuming and complex for novice users, we aim to simplify the decision-making process regarding…
Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the…
Compute Express Link (CXL) is a rapidly emerging coherent interconnect standard that provides opportunities for memory pooling and sharing. Memory sharing is a well-established software feature that improves memory utilization by avoiding…
Random Access is a critical procedure using which a User Equipment (UE) identifies itself to a Base Station (BS). Random Access starts with the UE transmitting a random preamble on the Physical Random Access Channel (PRACH). In a…
Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various engineering and scientific applications. However, inner product based SpGENN introduces redundant input fetches for mismatched nonzero operands, while…
An important receiver operation is to detect the presence specific preamble signals with unknown delays in the presence of scattering, Doppler effects and carrier offsets. This task, referred to as "link acquisition", is typically a…
The data acquisition system is based on ROOT and waveform digital technology, including neutron detector, waveform digitizer, PCI card, optical fiber, computer, reaction target device, stepper motor, data acquisition software and control…
Data and pipeline parallelism are key strategies for scaling neural network training across distributed devices, but their high communication cost necessitates co-located computing clusters with fast interconnects, limiting their…
Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources. To deal with…
Sparse matrix-matrix multiplication (SpGEMM) is a critical operation in numerous fields, including scientific computing, graph analytics, and deep learning. These applications exploit the sparsity of matrices to reduce storage and…