1,560 research outputs found
Selective Decoding in Associative Memories Based on Sparse-Clustered Networks
Associative memories are structures that can retrieve previously stored
information given a partial input pattern instead of an explicit address as in
indexed memories. A few hardware approaches have recently been introduced for a
new family of associative memories based on Sparse-Clustered Networks (SCN)
that show attractive features. These architectures are suitable for
implementations with low retrieval latency, but are limited to small networks
that store a few hundred data entries. In this paper, a new hardware
architecture of SCNs is proposed that features a new data-storage technique as
well as a method we refer to as Selective Decoding (SD-SCN). The SD-SCN has
been implemented using a similar FPGA used in the previous efforts and achieves
two orders of magnitude higher capacity, with no error-performance penalty but
with the cost of few extra clock cycles per data access.Comment: 4 pages, Accepted in IEEE Global SIP 2013 conferenc
Mining Dynamic Document Spaces with Massively Parallel Embedded Processors
Currently Océ investigates future document management services. One of these services is accessing dynamic document spaces, i.e. improving the access to document spaces which are frequently updated (like newsgroups). This process is rather computational intensive. This paper describes the research conducted on software development for massively parallel processors. A prototype has been built which processes streams of information from specified newsgroups and transforms them into personal information maps. Although this technology does speed up the training part compared to a general purpose processor implementation, however, its real benefits emerges with larger problem dimensions because of the scalable approach. It is recommended to improve on quality of the map as well as on visualisation and to better profile the performance of the other parts of the pipeline, i.e. feature extraction and visualisation
A Comparative Study of Sparse Associative Memories
We study various models of associative memories with sparse information, i.e.
a pattern to be stored is a random string of s and s with about
s, only. We compare different synaptic weights, architectures and retrieval
mechanisms to shed light on the influence of the various parameters on the
storage capacity.Comment: 28 pages, 2 figure
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
NASA JSC neural network survey results
A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc
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