720 research outputs found

    Plant invasions in China : an emerging hot topic in invasion science

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    China has shown a rapid economic development in recent decades, and several drivers of this change are known to enhance biological invasions, a major cause of biodiversity loss. Here we review the current state of research on plant invasions in China by analyzing papers referenced in the ISI Web of Knowledge. Since 2001, the number of papers has increased exponentially, indicating that plant invasions in China are an emerging hot topic in invasion science. The analyzed papers cover a broad range of methodological approaches and research topics. While more that 250 invasive plant species with negative impacts have been reported from China, only a few species have been considered in more than a handful of papers (in order of decreasing number of references: Spartina alterniflora, Ageratina adenophora, Mikania micrantha, Alternanthera philoxeroides, Solidago canadensis, Eichhornia crassipes). Yet this selection might rather reflect the location of research teams than the most invasive plant species in China. Considering the previous achievements in China found in our analysis research in plant invasions could be expanded by (1) compiling comprehensive lists of non-native plant species at the provincial and national scales and to include species that are native to one part of China but non-native to others in these lists; (2) strengthening pathways studies (primary introduction to the country, secondary releases within the country) to enhance prevention and management; and (3) assessing impacts of invasive species at different spatial scales (habitats, regions) and in relation to conservation resources

    GraphR: Accelerating Graph Processing Using ReRAM

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    This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suit- able for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms. GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01x (up to 132.67x) speedup and a 33.82x energy saving on geometric mean compared to a CPU baseline system. Com- pared to GPU, GRAPHR achieves 1.69x to 2.19x speedup and consumes 4.77x to 8.91x less energy. GRAPHR gains a speedup of 1.16x to 4.12x, and is 3.67x to 10.96x more energy efficiency compared to PIM-based architecture.Comment: Accepted to HPCA 201

    A Statistical STT-RAM Design View and Robust Designs at Scaled Technologies

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    Rapidly increased demands for memory in electronic industry and the significant technical scaling challenges of all conventional memory technologies motivated the researches on the next generation memory technology. As one promising candidate, spin-transfer torque random access memory (STT-RAM) features fast access time, high density, non-volatility, and good CMOS process compatibility. In recent years, many researches have been conducted to improve the storage density and enhance the scalability of STT-RAM, such as reducing the write current and switching time of magnetic tunneling junction (MTJ) devices. In parallel with these efforts, the continuous increasing of tunnel magneto-resistance(TMR) ratio of the MTJ inspires the development of multi-level cell (MLC) STT-RAM, which allows multiple data bits be stored in a single memory cell. Two types of MLC STT-RAM cells, namely, parallel MLC and series MLC, were also proposed. However, like all other nanoscale devices, the performance and reliability of STT-RAM cells are severely affected by process variations, intrinsic device operating uncertainties and environmental fluctuations. The storage margin of a MLC STT-RAM cell, i.e., the distinction between the lowest and highest resistance states, is partitioned into multiple segments for multi-level data representation. As a result, the performance and reliability of MLC STT-RAM cells become more sensitive to the MOS and MTJ device variations and the thermal-induced randomness of MTJ switching. In this work, we systematically analyze the impacts of CMOS and MTJ process variations, MTJ resistance switching randomness that induced by intrinsic thermal fluctuations, and working temperature changes on STT-RAM cell designs. The STT-RAM cell reliability issues in both read and write operations are first investigated. A combined circuit and magnetic simulation platform is then established to quantitatively study the persistent and non-persistent errors in STT-RAM cell operations. Then, we analyzed the extension of STT-RAM cell behaviors from SLC (single-level- cell) to MLC (multi-level- cell). On top of that, we also discuss the optimal device parameters of the MLC MTJ for the minimization of the operation error rate of the MLC STT-RAM cells from statistical design perspective. Our simulation results show that under the current available technology, series MLC STT-RAM demonstrates overwhelming benefits in the read and write reliability compared to parallel MLC STT-RAM and could potentially satisfy the requirement of commercial practices. Finally, with the detail analysis study of STT-RAM cells, we proposed several error reduction design, such as ADAMS structure, and FA-STT structure
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