scholarly journals Faster learning by reduction of data access time

2018 ◽  
Vol 48 (12) ◽  
pp. 4715-4729 ◽  
Author(s):  
Vinod Kumar Chauhan ◽  
Anuj Sharma ◽  
Kalpana Dahiya
Keyword(s):  
2021 ◽  
Vol 18 (3) ◽  
pp. 1-22
Author(s):  
Michael Stokes ◽  
David Whalley ◽  
Soner Onder

While data filter caches (DFCs) have been shown to be effective at reducing data access energy, they have not been adopted in processors due to the associated performance penalty caused by high DFC miss rates. In this article, we present a design that both decreases the DFC miss rate and completely eliminates the DFC performance penalty even for a level-one data cache (L1 DC) with a single cycle access time. First, we show that a DFC that lazily fills each word in a DFC line from an L1 DC only when the word is referenced is more energy-efficient than eagerly filling the entire DFC line. For a 512B DFC, we are able to eliminate loads of words into the DFC that are never referenced before being evicted, which occurred for about 75% of the words in 32B lines. Second, we demonstrate that a lazily word filled DFC line can effectively share and pack data words from multiple L1 DC lines to lower the DFC miss rate. For a 512B DFC, we completely avoid accessing the L1 DC for loads about 23% of the time and avoid a fully associative L1 DC access for loads 50% of the time, where the DFC only requires about 2.5% of the size of the L1 DC. Finally, we present a method that completely eliminates the DFC performance penalty by speculatively performing DFC tag checks early and only accessing DFC data when a hit is guaranteed. For a 512B DFC, we improve data access energy usage for the DTLB and L1 DC by 33% with no performance degradation.


Author(s):  
S.Tamil Selvan ◽  
M. Sundararajan

In this paper presented Design and implementation of CNTFET based Ternary 1x1 RAM memories high-performance digital circuits. CNTFET Ternary 1x1 SRAM memories is implement using 32nm technology process. The CNTFET decresase the diameter and performance matrics like delay,power and power delay, The CNTFET Ternary 6T SRAM cell consists of two cross coupled Ternary inverters one is READ and another WRITE operations of the Ternary 6T SRAM cell are performed with the Tritline using HSPICE and Tanner tools in this tool is performed high accuracy. The novel based work can be used for Low Power Application and Access time is less of compared to the conventional CMOS Technology. The CNTFET Ternary 6T SRAM array module (1X1) in 32nm technology consumes only 0.412mW power and data access time is about 5.23ns.


Author(s):  
Mary Magdalene Jane.F ◽  
R. Nadarajan ◽  
Maytham Safar

Data caching in mobile clients is an important technique to enhance data availability and improve data access time. Due to cache size limitations, cache replacement policies are used to find a suitable subset of items for eviction from the cache. In this paper, the authors study the issues of cache replacement for location-dependent data under a geometric location model and propose a new cache replacement policy RAAR (Re-entry probability, Area of valid scope, Age, Rate of Access) by taking into account the spatial and temporal parameters. Mobile queries experience a popularity drift where the item loses its popularity after the user exhausts the corresponding service, thus calling for a scenario in which once popular documents quickly become cold (small active sets). The experimental evaluations using synthetic datasets for regular and small active sets show that this replacement policy is effective in improving the system performance in terms of the cache hit ratio of mobile clients.


2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Mohammed A. S. Mosleh ◽  
G. Radhamani ◽  
Mohamed A. G. Hazber ◽  
Syed Hamid Hasan

Task execution in cloud computing requires obtaining stored data from remote data centers. Though this storage process reduces the memory constraints of the user’s computer, the time deadline is a serious concern. In this paper, Adaptive Cost-based Task Scheduling (ACTS) is proposed to provide data access to the virtual machines (VMs) within the deadline without increasing the cost. ACTS considers the data access completion time for selecting the cost effective path to access the data. To allocate data access paths, the data access completion time is computed by considering the mean and variance of the network service time and the arrival rate of network input/output requests. Then the task priority is assigned to the removed tasks based data access time. Finally, the cost of data paths are analyzed and allocated based on the task priority. Minimum cost path is allocated to the low priority tasks and fast access path are allocated to high priority tasks as to meet the time deadline. Thus efficient task scheduling can be achieved by using ACTS. The experimental results conducted in terms of execution time, computation cost, communication cost, bandwidth, and CPU utilization prove that the proposed algorithm provides better performance than the state-of-the-art methods.


Author(s):  
Kamel Aouiche ◽  
Jérôme Darmont

Database management systems (DBMSs) require an administrator whose principal tasks are data management, both at the logical and physical levels, as well as performance optimization. With the wide development of databases and data warehouses, minimizing the administration function is crucial. This function includes the selection of suitable physical structures to improve system performance. View materialization and indexing are presumably some of the most effective optimization techniques adopted in relational implementations of data warehouses. Materialized views are physical structures that improve data access time by precomputing intermediary results. Therefore, end-user queries can be efficiently processed through data stored in views and do not need to access the original data. Indexes are also physical structures that allow direct data access. They avoid sequential scans and thereby reduce query response time. Nevertheless, these solutions require additional storage space and entail maintenance overhead. The issue is then to select an appropriate configuration of materialized views and indexes that minimizes both query response time and maintenance cost given a limited storage space. This problem is NP hard (Gupta & Mumick, 2005).


Author(s):  
Marcin Kuta ◽  
Darin Nikolow ◽  
Renata Słota ◽  
Jacek Kitowski

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