Variability analysis of resistive ternary content addressable memories

2020 ◽  
Author(s):  
Mohamed A. Bahloul ◽  
Mohammed E. Fouda ◽  
Imen Barraj ◽  
Mohamed Masmoudi
Keyword(s):  
Variability Analysis ◽  
Content Addressable Memories

Variability Analysis of Climate Extreme Index using Downscaled Multi-models and Grid-based CMIP5 Climate Change Scenario Data

2020 ◽  
Vol 11 (2) ◽  
pp. 123-132
Author(s):  
Jae-Pil Cho ◽  
Jae-Uk Kim ◽  
Soon-Kun Choi ◽  
Sye-Woon Hwang ◽  
Hui-Cheul Jung
Keyword(s):  
Climate Change ◽  
Climate Change Scenario ◽  
Extreme Index ◽  
Change Scenario ◽  
Climate Extreme ◽  
Variability Analysis ◽  
Grid Based

Content-Addressable Memories

1987 ◽  
Author(s):  
Teuvo Kohonen
Keyword(s):  
Content Addressable Memories

Performance Variability Analysis of PhotonicCircuits with Many Correlated Parameters

2021 ◽  
pp. 1-1
Author(s):  
Abi Waqas ◽  
Paolo Manfredi ◽  
Daniele Melati
Keyword(s):  
Variability Analysis ◽  
Performance Variability ◽  
Correlated Parameters

scholarly journals Efficient local locking for massively multithreaded in-memory hash-based operators

2021 ◽  
Author(s):  
Bashar Romanous ◽  
Skyler Windh ◽  
Ildar Absalyamov ◽  
Prerna Budhkar ◽  
Robert Halstead ◽  
...  
Keyword(s):  
Relational Databases ◽  
Aggregation Operators ◽  
Main Memory ◽  
Paradigm Shifts ◽  
Multithreaded Processors ◽  
Cache Hierarchies ◽  
Processor Architectures ◽  
Spatial Locality ◽  
Content Addressable Memories ◽  
Multi Core Processor

AbstractThe join and group-by aggregation are two memory intensive operators that are affecting the performance of relational databases. Hashing is a common approach used to implement both operators. Recent paradigm shifts in multi-core processor architectures have reinvigorated research into how the join and group-by aggregation operators can leverage these advances. However, the poor spatial locality of the hashing approach has hindered performance on multi-core processor architectures which rely on using large cache hierarchies for latency mitigation. Multithreaded architectures can better cope with poor spatial locality by masking memory latency with many outstanding requests. Nevertheless, the number of parallel threads, even in the most advanced multithreaded processors, such as UltraSPARC, is not enough to fully cover the main memory access latency. In this paper, we explore the hardware re-configurability of FPGAs to enable deeper execution pipelines that maintain hundreds (instead of tens) of outstanding memory requests across four FPGAs-drastically increasing concurrency and throughput. We present two end-to-end in-memory accelerators for the join and group-by aggregation operators using FPGAs. Both accelerators use massive multithreading to mask long memory delays of traversing linked-list data structures, while concurrently managing hundreds of thread states across four FPGAs locally. We explore how content addressable memories can be intermixed within our multithreaded designs to act as a synchronizing cache, which enforces locks and merges jobs together before they are written to memory. Throughput results for our hash-join operator accelerator show a speedup between 2$$\times $$ × and 3.4$$\times $$ × over the best multi-core approaches with comparable memory bandwidths on uniform and skewed datasets. The accelerator for the hash-based group-by aggregation operator demonstrates that leveraging CAMs achieves average speedup of 3.3$$\times $$ × with a best case of 9.4$$\times $$ × in terms of throughput over CPU implementations across five types of data distributions.

Some applications for content-addressable memories

1963 ◽  
Author(s):  
G. Estrin ◽  
R. H. Fuller
Keyword(s):  
Content Addressable Memories

scholarly journals Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ricardo A. Gonzales ◽  
Felicia Seemann ◽  
Jérôme Lamy ◽  
Per M. Arvidsson ◽  
Einar Heiberg ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Active Contours ◽  
Imaging Biomarkers ◽  
Observer Variability ◽  
Time Resolved ◽  
Variability Analysis ◽  
Automated Method ◽  
Inter Observer Variability ◽  
Atrial Size ◽  
And Function

Abstract Background Segmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manually, which is time-consuming and observer-dependent. Methods This study presents an automated image processing algorithm for time-resolved LA segmentation in cardiac magnetic resonance imaging (MRI) long-axis cine images of the 2-chamber (2ch) and 4-chamber (4ch) views using active contours. The proposed algorithm combines mitral valve tracking, automated threshold calculation, edge detection on a radially resampled image, edge tracking based on Dijkstra’s algorithm, and post-processing involving smoothing and interpolation. The algorithm was evaluated in 37 patients diagnosed mainly with paroxysmal atrial fibrillation. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), with manual segmentations in all time frames as the reference standard. For inter-observer variability analysis, a second observer performed manual segmentations at end-diastole and end-systole on all subjects. Results The proposed automated method achieved high performance in segmenting the LA in long-axis cine sequences, with a DSC of 0.96 for 2ch and 0.95 for 4ch, and an HD of 5.5 mm for 2ch and 6.4 mm for 4ch. The manual inter-observer variability analysis had an average DSC of 0.95 and an average HD of 4.9 mm. Conclusion The proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function.

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