Efficient Mapping without Deadlock on the Many-core Neural Network Chip

Author(s):  
Qi Zhao ◽  
Lei Deng ◽  
Guoqi Li ◽  
Guanrui Wang ◽  
Cheng Ma
Keyword(s):  
The Many ◽  
2020 ◽  
Vol 401 ◽  
pp. 327-337
Author(s):  
Cheng Ma ◽  
Qi Zhao ◽  
Guoqi Li ◽  
Lei Deng ◽  
Guanrui Wang

Author(s):  
Paul A. Bramadat

Throughout the previous three chapters, I have introduced (i) the set of questions A I am asking in this book, (2) four members of the IVCF, and (3) the ways these believers communicate among themselves and with non-Christians. By now it should be clear that IVCF students often feel separated from their non-Christian peers and professors. Moreover, as I have explained, many IVCF students feel that McMaster privileges the beliefs, values, and worldviews associated with liberalism, pluralism, materialism, and permissivism. According to Reginald Bibby, this evangelical perception is largely correct: . . . Education stands out as an institution that not only has been strongly influenced by individualism and relativism but also has done much to legitimize the two themes. Indeed, the mark of a well-educated Canadian is that he or she places supreme importance on the individual while recognizing that truth is relative. To decry individual fulfilment or to claim to have found the truth would be a dead giveaway that one has not graced the halls of higher learning. (1990:71) . . . This situation marginalizes, alienates, or (to make a verb of an adjective) others evangelical students who generally do not embrace these traditions (or many core elements of these traditions). However, although it might appear that IVCF students would suffer unrelenting and agonizing psychological difficulties during their years at McMaster, the majority of IVCF members do not seem to share such an experience. On the contrary, most IVCF participants I met struck me as no less sane, healthy, contented, and well adjusted than the non-Christian students I have met during the many years I have spent in Canadian universities. In fact, I have found that, with a few exceptions, evangelicals at McMaster seem slightly “happier” than non-Christian students. This obviously unscientific impression is consistent with Frankel and Hewitt’s (1994) findings that involvement in religious groups during one’s university years is positively correlated with higher levels of physical and psychological “well-being.” This observation raises an obvious question: how do evangelicals retain these relatively high levels of psychological well-being in an institution that not only ignores their values and beliefs but also, according to IVCF students, often promotes “anti-Christian” principles? The main insiders’ (or “emic”) answer to this question is simply that well-being is a natural by-product of a personal relationship with God (Little 1988:38).


Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1342
Author(s):  
Gianvito Urgese ◽  
Francesco Barchi ◽  
Emanuele Parisi ◽  
Evelina Forno ◽  
Andrea Acquaviva ◽  
...  

SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform.


VLSI Design ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Arezoo Kamran ◽  
Zainalabedin Navabi

More pronounced aging effects, more frequent early-life failures, and incomplete testing and verification processes due to time-to-market pressure in new fabrication technologies impose reliability challenges on forthcoming systems. A promising solution to these reliability challenges is self-test and self-reconfiguration with no or limited external control. In this work a scalable self-test mechanism for periodic online testing of many-core processor has been proposed. This test mechanism facilitates autonomous detection and omission of faulty cores and makes graceful degradation of the many-core architecture possible. Several test components are incorporated in the many-core architecture that distribute test stimuli, suspend normal operation of individual processing cores, apply test, and detect faulty cores. Test is performed concurrently with the system normal operation without any noticeable downtime at the application level. Experimental results show that the proposed test architecture is extensively scalable in terms of hardware overhead and performance overhead that makes it applicable to many-cores with more than a thousand processing cores.


2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Martyna Sasiada ◽  
Aneta Fraczek-Szczypta ◽  
Ryszard Tadeusiewicz

AbstractA new method of predicting the properties of carbon nanomaterials from carbon nanotubes and graphene oxide, using electrophoretic deposition (EPD) on a metal surface, was investigated. The main goal is to obtain the basis for nervous tissue stimulation and regeneration. Because of the many variations of the EPD method, costly and time-consuming experiments are necessary for optimization of the produced systems. To limit such costs and workload, we propose a neural network-based model that can predict the properties of selected carbon nanomaterial systems before they are produced. The choice of neural networks as predictive learning models is based on many studies in the literature that report neural models as good interpretations of real-life processes. The use of a neural network model can reduce experimentation with unpromising methods of systems processing and preparation. Instead, it allows a focus on experiments with these systems, which are promising according to the prediction given by the neural model. The performed tests showed that the proposed method of predictive learning of carbon nanomaterial properties is easy and effective. The experiments showed that the prediction results were consistent with those obtained in the real system.


2011 ◽  
Vol 46 (11) ◽  
pp. 77-78 ◽  
Author(s):  
Onur Mutlu
Keyword(s):  
The Many ◽  

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