Dynamic random-access memory (DRAM) is the building block of modern main memory systems. DRAM cells must be periodically refreshed to prevent loss of data. These refresh operations waste energy and degrade system performance by interfering with memory accesses. The negative effects of DRAM refresh increase as DRAM device capacity increases. Existing DRAM devices refresh all cells at a rate determined by the leakiest cell in the device. However, most DRAM cells can retain data for significantly longer. Therefore, many of these refreshes are unnecessary. In this paper, we propose RAIDR (Retention-Aware Intelligent DRAM Refresh), a low-cost mechanism that can identify and skip unnecessary refreshes using knowledge of cell retention times. Our key idea is to group DRAM rows into retention time bins and apply a different refresh rate to each bin. As a result, rows containing leaky cells are refreshed as frequently as normal, while most rows are refreshed less frequently. RAIDR uses Bloom filters to efficiently implement retention time bins. RAIDR requires no modification to DRAM and minimal modification to the memory controller. In an 8-core system with 32 GB DRAM, RAIDR achieves a 74.6% refresh reduction, an average DRAM power reduction of 16.1%, and an average system performance improvement of 8.6% over existing systems, at a modest storage overhead of 1.25 KB in the memory controller. RAIDR's benefits are robust to variation in DRAM system configuration, and increase as memory capacity increases.

/pdf/raidr-retention-aware-intelligent-dram-refresh-4t380u711e.pdf

RAIDR: Retention-Aware Intelligent DRAM Refresh

Knowing the distribution of the sizes of traffic flows passing through a network link helps a network operator to characterize network resource usage, infer traffic demands, detect traffic anomalies, and accommodate new traffic demands through better traffic engineering. Previous work on estimating the flow size distribution has been focused on making inferences from sampled network traffic. Its accuracy is limited by the (typically) low sampling rate required to make the sampling operation affordable. In this paper we present a novel data streaming algorithm to provide much more accurate estimates of flow distribution, using a "lossy data structure" which consists of an array of counters fitted well into SRAM. For each incoming packet, our algorithm only needs to increment one underlying counter, making the algorithm fast enough even for 40 Gbps (OC-768) links. The data structure is lossy in the sense that sizes of multiple flows may collide into the same counter. Our algorithm uses Bayesian statistical methods such as Expectation Maximization to infer the most likely flow size distribution that results in the observed counter values after collision. Evaluations of this algorithm on large Internet traces obtained from several sources (including a tier-1 ISP) demonstrate that it has very high measurement accuracy (within 2%). Our algorithm not only dramatically improves the accuracy of flow distribution measurement, but also contributes to the field of data streaming by formalizing an existing methodology and applying it to the context of estimating the flow-distribution.

Data streaming algorithms for efficient and accurate estimation of flow size distribution

A data storage and retrieval device and method is disclosed. The device includes at least one magnetic storage medium configured to store target data and at least one re-configurable logic device comprising an FPGA coupled to the at least one magnetic storage medium and configured to read a continuous stream of target data therefrom, having been configured with a template or as otherwise desired to fit the type of search and data being searched. The re-configurable logic device is configured to receive at least one search inquiry in the form of a data key and to determine a match between the data key and the target data as it is being read from the at least one magnetic storage medium. This device and method can perform a variety of searches on the target data including without limitation exact and approximate match searches, sequence match searches, image match searches and data reduction searches. This device and method may be provided as part of a stand-alone computer system, embodied in a network attached storage device, or can otherwise be provided as part of a computer LAN or WAN. In addition to performing search and data reduction operations, this device may also be used to perform a variety of other processing operations including encryption, decryption, compression, decompression, and combinations thereof.

Intelligent data storage and processing using fpga devices

Disclosed herein is an improved architecture for regular expression pattern matching. Improvements to pattern matching deterministic finite automatons (DFAs) that are described by the inventors include a pipelining strategy that pushes state-dependent feedback to a final pipeline stage to thereby enhance parallelism and throughput, augmented state transitions that track whether a transition is indicative of a pattern match occurring thereby reducing the number of necessary states for the DFA, augmented state transition that track whether a transition is indicative of a restart to the matching process, compression of the DFA's transition table, alphabet encoding for input symbols to equivalence class identifiers, the use of an indirection table to allow for optimized transition table memory, and enhanced scalability to facilitate the ability of the improved DFA to process multiple input symbols per cycle.

Method and device for high performance regular expression pattern matching

Methods and systems for processing financial market data using reconfigurable logic are disclosed. Various functional operations to be performed on the financial market data can be implemented in firmware pipelines to accelerate the speed of processing. Also, a combination of software logic and firmware logic can be used to efficiently control and manage the high speed flow of financial market data to and from the reconfigurable logic.

High Speed Processing of Financial Information Using FPGA Devices

Hashing is critical for high performance computer architecture. Hashing is used extensively in hardware applications, such as page tables, for address translation. Bit extraction and exclusive ORing hashing "methods" are two commonly used hashing functions for hardware applications. There is no study of the performance of these functions and no mention anywhere of the practical performance of the hashing functions in comparison with the theoretical performance prediction of hashing schemes. In this paper, we show that, by choosing hashing functions at random from a particular class, called H/sub 3/, of hashing functions, the analytical performance of hashing can be achieved in practice on real-life data. Our results about the expected worst case performance of hashing are of special significance, as they provide evidence for earlier theoretical predictions.

Efficient hardware hashing functions for high performance computers

Technology acceptance studies are interesting because they are practical and theoretically helpful in explaining the adoption and intention to use a particular technology. There is a large amount of research on e-learning and other technologies in the literature, but there is limited evidence to explain why secondary school students’ intention to use e-learning. This study explains secondary school students’ intentions to use e-learning with an extended Technology Acceptance Model (TAM). TAM is a useful theory to explain how people adopt new technologies in different fields. Data were collected from 2739 secondary school students in Turkey (Mage = 11.95). Confirmatory factor analysis (CFA) and structural equation modelling (SEM) were used to test the conceptual model. The results are consistent with the original TAM model. The most critical variable affecting secondary school students’ intention to use e-learning technologies is enjoyment. The results show that there may be differences in the intention to use e-learning technologies for secondary school students in different cultures and contexts.

Predicting the secondary school students’ intention to use e-learning technologies

Digital literacy is a critical skill that administrative staff must acquire to continue their business activities at higher education. However, digital literacy studies for administrative staff seem to be neglected in the current literature. This article examines the impact of higher education administrative staff’s digital literacy on their intention to use digital technologies while performing their tasks. For this purpose, a conceptual model consisting of effort expectancy and performance expectancy structures based on the unified theory of acceptance and use of technology and expanded with the digital literacy dimension has been created. Data were collected from 158 participants who were administrative staff of two higher education institutions in Türkiye to evaluate the theoretical model. The data were analysed using the structural equation modelling technique. The findings revealed the relationship between the digital literacy skills of higher education administrative staff and their intention to use digital technology. According to the results, digital literacy has a direct effect on effort expectancy but not on performance expectancy. Also, contrary to our expectations, digital literacy does not directly affect the intention to use. However, digital literacy affects the intention to use digital technology through effort expectancy and performance expectancy. In higher education, personnel with low digital literacy skills should be identified and in-service training should be provided. This is one of the first studies to address the impact of digital literacy on technology acceptance by administrative staff working in higher education.

The effect of digital literacy on technology acceptance: An evaluation on administrative staff in higher education

: This study reports on a survey of 4 - 8th grade students’ (N=10139) playing game tendencies and digital game preferences based on gender. Descriptive statistics were used to determine the children’s playing game tendencies and digital game preferences. And also, frequent pattern mining was used to discover which digital games are played frequently and together by children. The findings showed that children have an increased time spent playi ng games on weekends and boys tend to playing game more than girls. On the other hand, action games stood out as the most common choice among the children both girls and boys. In terms of game language, Turkish and English games were preferred almost equally. In addition, children mostly preferred digital games that can be played online, offering in -app purchases, and played on mobile devices. According to the frequency of playing, it was found that the children mostly preferred to play Minecraft , followed by Subway Surfers and Hay Day , respectively. In terms of gender, it was concluded that girls mostly preferred Subway Surfers , while boys played Hay Day . When the games were considered according to the frequency of playing together, Temple Run and Subway Surfers games among girls, and FIFA and Piano Tiles games among boys can be played together. The research reveals more detailed information about children’s game preferences in terms of gender.

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E. Bahcekapili

Papers

Efficient hardware hashing functions for high performance computers

Predicting the secondary school students’ intention to use e-learning technologies

The effect of digital literacy on technology acceptance: An evaluation on administrative staff in higher education

Examination of Children’ Playing Game Tendencies and Digital Game Preferences Based on Gender