A dynamic Verilog-A resistive random access memory (RRAM) compact model, including cycle-to-cycle variation, is developed for circuit/system explorations. The model not only captures dc and ac ...behavior, but also includes intrinsic random fluctuations and variations. A methodology to systematically calibrate the model parameters with experiments is presented and illustrated with a broad set of experimental data, including multilayer RRAM. The physical meanings of the various model parameters are discussed. An example of applying the RRAM cell model to a ternary content-addressable-memory (TCAM) macro is provided. Tradeoffs on the design of RRAM devices for the TCAM macro are discussed in the context of the energy consumption and worst case latency of the memory array.
Lacking of adaptation to various array imperfections is an open problem for most high-precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods are data-driven, they do ...not rely on prior assumptions about array geometries, and are expected to adapt better to array imperfections when compared with model-based counterparts. This paper introduces a framework of the deep neural network to address the DOA estimation problem, so as to obtain good adaptation to array imperfections and enhanced generalization to unseen scenarios. The framework consists of a multitask autoencoder and a series of parallel multilayer classifiers. The autoencoder acts like a group of spatial filters, it decomposes the input into multiple components in different spatial subregions. These components thus have more concentrated distributions than the original input, which helps to reduce the burden of generalization for subsequent DOA estimation classifiers. The classifiers follow a one-versus-all classification guideline to determine if there are signal components near preseted directional grids, and the classification results are concatenated to reconstruct a spatial spectrum and estimate signal directions. Simulations are carried out to show that the proposed method performs satisfyingly in both generalization and imperfection adaptation.
Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods ...mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on 12 multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. The code is available at https://github.com/youweiliang/Multi-view_Graph_Learning .
Differential privacy is an essential and prevalent privacy model that has been widely explored in recent decades. This survey provides a comprehensive and structured overview of two research ...directions: differentially private data publishing and differentially private data analysis. We compare the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy. We identify two basic frameworks for differentially private data analysis and list the typical algorithms used within each framework. The results are compared and discussed based on output accuracy and efficiency. Further, we propose several possible directions for future research and possible applications.
Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training ...process of CNNs is very time-consuming, where large amounts of training samples and iterative operations are required to obtain high-quality weight parameters. In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments. BPT-CNN consists of two main components: (a) an outer-layer parallel training for multiple CNN subnetworks on separate data subsets, and (b) an inner-layer parallel training for each subnetwork. In the outer-layer parallelism, we address critical issues of distributed and parallel computing, including data communication, synchronization, and workload balance. A heterogeneous-aware Incremental Data Partitioning and Allocation (IDPA) strategy is proposed, where large-scale training datasets are partitioned and allocated to the computing nodes in batches according to their computing power. To minimize the synchronization waiting during the global weight update process, an Asynchronous Global Weight Update (AGWU) strategy is proposed. In the inner-layer parallelism, we further accelerate the training process for each CNN subnetwork on each computer, where computation steps of convolutional layer and the local weight training are parallelized based on task-parallelism. We introduce task decomposition and scheduling strategies with the objectives of thread-level load balancing and minimum waiting time for critical paths. Extensive experimental results indicate that the proposed BPT-CNN effectively improves the training performance of CNNs while maintaining the accuracy.
The emerging paradigm of 'abundant-data' computing requires real-time analytics on enormous quantities of data collected by a mushrooming network of sensors. Todays computing technology, however, ...cannot scale to satisfy such big data applications with the required throughput and energy efficiency. The next technology frontier will be monolithically integrated chips with three-dimensionally interleaved memory and logic for unprecedented data bandwidth with reduced energy consumption. In this work, we exploit the atomically thin nature of the graphene edge to assemble a resistive memory (∼ 3 Å thick) stacked in a vertical three-dimensional structure. We report some of the lowest power and energy consumption among the emerging non-volatile memories due to an extremely thin electrode with unique properties, low programming voltages, and low current. Circuit analysis of the three-dimensional architecture using experimentally measured device properties show higher storage potential for graphene devices compared that of metal based devices.
Abstract
The most promising variation of the standard siren technique combines gravitational-wave (GW) data for binary neutron star (BNS) mergers with redshift measurements enabled by their ...electromagnetic (EM) counterparts, to constrain cosmological parameters such as
H
0
, Ω
m
, and
w
0
. Here we evaluate the near- and long-term prospects of multimessenger cosmology in the era of future GW observatories: Advanced LIGO Plus (A+, 2025), Voyager-like detectors (2030s), and Cosmic Explorer–like detectors (2035 and beyond). We show that the BNS horizon distance of ≈ 700 Mpc for A+ is well matched to the sensitivity of the Vera C. Rubin Observatory (VRO) for kilonova detections. We find that one year of joint A+ and VRO observations will constrain the value of
H
0
to percent-level precision, given a small investment of VRO time dedicated to target-of-opportunity GW follow-up. In the Voyager era, the BNS–kilonova observations begin to constrain Ω
m
with an investment of a few percent of VRO time. With the larger BNS horizon distance in the Cosmic Explorer era, on-axis short gamma-ray bursts (SGRBs) and their afterglows (though accompanying only some of the GW-detected mergers) supplant kilonovae as the most promising counterparts for redshift identification. We show that five years of joint observations with Cosmic Explorer–like facilities and a next-generation gamma-ray satellite with localization capabilities similar to that presently possible with Swift could constrain both Ω
m
and
w
0
to 15%–20%. We therefore advocate for a robust target-of-opportunity (ToO) program with VRO, and a wide-field gamma-ray satellite with improved sensitivity in the 2030s, to enable standard siren cosmology with next-generation gravitational-wave facilities.
PDF
