14,025 research outputs found
McSimA+: A Manycore Simulator with Application-level+ Simulation and Detailed Microarchitecture Modeling
Abstract-With their significant performance and energy advantages, emerging manycore processors have also brought new challenges to the architecture research community. Manycore processors are highly integrated complex system-on-chips with complicated core and uncore subsystems. The core subsystems can consist of a large number of traditional and asymmetric cores. The uncore subsystems have also become unprecedentedly powerful and complex with deeper cache hierarchies, advanced on-chip interconnects, and high-performance memory controllers. In order to conduct research for emerging manycore processor systems, a microarchitecture-level and cycle-level manycore simulation infrastructure is needed. This paper introduces McSimA+, a new timing simulation infrastructure, to meet these needs. McSimA+ models x86-based asymmetric manycore microarchitectures in detail for both core and uncore subsystems, including a full spectrum of asymmetric cores from single-threaded to multithreaded and from in-order to out-of-order, sophisticated cache hierarchies, coherence hardware, on-chip interconnects, memory controllers, and main memory. McSimA+ is an application-level+ simulator, offering a middle ground between a full-system simulator and an application-level simulator. Therefore, it enjoys the light weight of an application-level simulator and the full control of threads and processes as in a full-system simulator. This paper also explores an asymmetric clustered manycore architecture that can reduce the thread migration cost to achieve a noticeable performance improvement compared to a state-of-the-art asymmetric manycore architecture
Neural Distributed Autoassociative Memories: A Survey
Introduction. Neural network models of autoassociative, distributed memory
allow storage and retrieval of many items (vectors) where the number of stored
items can exceed the vector dimension (the number of neurons in the network).
This opens the possibility of a sublinear time search (in the number of stored
items) for approximate nearest neighbors among vectors of high dimension. The
purpose of this paper is to review models of autoassociative, distributed
memory that can be naturally implemented by neural networks (mainly with local
learning rules and iterative dynamics based on information locally available to
neurons). Scope. The survey is focused mainly on the networks of Hopfield,
Willshaw and Potts, that have connections between pairs of neurons and operate
on sparse binary vectors. We discuss not only autoassociative memory, but also
the generalization properties of these networks. We also consider neural
networks with higher-order connections and networks with a bipartite graph
structure for non-binary data with linear constraints. Conclusions. In
conclusion we discuss the relations to similarity search, advantages and
drawbacks of these techniques, and topics for further research. An interesting
and still not completely resolved question is whether neural autoassociative
memories can search for approximate nearest neighbors faster than other index
structures for similarity search, in particular for the case of very high
dimensional vectors.Comment: 31 page
The Algebraic View of Computation
We argue that computation is an abstract algebraic concept, and a computer is
a result of a morphism (a structure preserving map) from a finite universal
semigroup.Comment: 13 pages, final version will be published elsewher
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