Active Memory Cube: A processing-in-memory architecture for exascale systems

2015 ◽  
Vol 59 (2/3) ◽  
pp. 17:1-17:14 ◽  
Author(s):  
R. Nair ◽  
S. F. Antao ◽  
C. Bertolli ◽  
P. Bose ◽  
J. R. Brunheroto ◽  
...  
Keyword(s):  
Memory Architecture ◽  
Active Memory

scholarly journals Intrinsic content, active memory and the extended mind

Analysis ◽  
2005 ◽  
Vol 65 (1) ◽  
pp. 1-11 ◽  
Author(s):  
A. Clark
Keyword(s):  
Extended Mind ◽  
Active Memory

Return to Cognitive Science

2018 ◽  
Author(s):  
James A. Anderson
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Machine Intelligence ◽  
Active Memory ◽  
Practical Project ◽  
Digital Computers

Is ambiguity unavoidable? It is found in vision and everywhere in language. Semantic nets for disambiguation are realized in George Miller’s WordNet, a practical project helping disambiguate search strings using contextual disambiguation. Simple association using traditional passive memory is boring compared to complex association using active memory with multiple associative links active at the same time to perform a clearly defined task. A “mixer” is used to recognize items from a list, and generalization of the mixer is used for disambiguation. The chapter also discusses artificial intelligence, both its origins and currently ignored questions: Are biological intelligence and machine intelligence the same thing? Can digital computers really mimic in digital software a largely analog brain? The important question is not why machines are becoming so smart but why humans are still so good. Artificial intelligence is missing something important probably based on hardware differences.

scholarly journals Directed forgetting in rats: Evidence for active memory control?

2019 ◽  
Vol 47 (4) ◽  
pp. 310-325
Author(s):  
Chiaki Tanaka ◽  
Hayato Yahagi ◽  
Tohru Taniuchi
Keyword(s):  
Directed Forgetting ◽  
Active Memory ◽  
Memory Control

A Study on the Performance and Scalability of Apache Flink Over Hadoop MapReduce

2019 ◽  
Vol 2 (1) ◽  
pp. 61-73
Author(s):  
Pankaj Lathar ◽  
K. G. Srinivasa
Keyword(s):  
Comparative Analysis ◽  
Parallel Processing ◽  
Data Processing ◽  
Real World ◽  
Memory Management ◽  
Programming Model ◽  
Raw Data ◽  
Hadoop Mapreduce ◽  
Active Memory ◽  
Real World Problems

With the advancements in science and technology, data is being generated at a staggering rate. The raw data generated is generally of high value and may conceal important information with the potential to solve several real-world problems. In order to extract this information, the raw data available must be processed and analysed efficiently. It has however been observed, that such raw data is generated at a rate faster than it can be processed by traditional methods. This has led to the emergence of the popular parallel processing programming model – MapReduce. In this study, the authors perform a comparative analysis of two popular data processing engines – Apache Flink and Hadoop MapReduce. The analysis is based on the parameters of scalability, reliability and efficiency. The results reveal that Flink unambiguously outperformance Hadoop's MapReduce. Flink's edge over MapReduce can be attributed to following features – Active Memory Management, Dataflow Pipelining and an Inline Optimizer. It can be concluded that as the complexity and magnitude of real time raw data is continuously increasing, it is essential to explore newer platforms that are adequately and efficiently capable of processing such data.

scholarly journals Microscopic colitis patients have increased proportions of Ki67+ proliferating and CD45RO+ active/memory CD8+ and CD4+8+ mucosal T cells

2013 ◽  
Vol 7 (9) ◽  
pp. 694-705 ◽  
Author(s):  
Ashok Kumar Kumawat ◽  
Hilja Strid ◽  
Kristina Elgbratt ◽  
Curt Tysk ◽  
Johan Bohr ◽  
...  
Keyword(s):  
T Cells ◽  
Microscopic Colitis ◽  
Active Memory ◽  
Mucosal T Cells
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