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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 16:00:41 +0000A Novel Associative Memory Implemented Using Collective Computation
https://resolver.caltech.edu/CaltechAUTHORS:20150310-154028014
Authors: {'items': [{'id': 'Sivilotti-M-A', 'name': {'family': 'Sivilotti', 'given': 'Massimo'}}, {'id': 'Emerling-M', 'name': {'family': 'Emerling', 'given': 'Michael'}}, {'id': 'Mead-C-A', 'name': {'family': 'Mead', 'given': 'Carver'}}]}
Year: 1985
A radically new type of associative memory, the ASSOCMEM, has been implemented in VLSI and tested. Analog circuit techniques are used to construct a network that evolves towards fully restored (digital) fixed-points that are the memories of the system. Association occurs
on the whole source word, each bit of which may assume a continuous analog value. The network does not require the distinction of a search key from a data field in either the source or target words. A key may be dynamically defined by differentially weighting any subset of the source
word. The key need not be exact; the system will evolve to the closest memory. In the case when the key is the whole input word, the system may be thought of as performing error correction.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/n853j-1am56VLSI architectures for implementation of neural networks
https://resolver.caltech.edu/CaltechAUTHORS:20141215-164438997
Authors: {'items': [{'id': 'Sivilotti-M-A', 'name': {'family': 'Sivilotti', 'given': 'Massimo A.'}}, {'id': 'Emerling-M-R', 'name': {'family': 'Emerling', 'given': 'Michael R.'}}, {'id': 'Mead-C-A', 'name': {'family': 'Mead', 'given': 'Carver A.'}}]}
Year: 1986
DOI: 10.1063/1.36247
A large scale collective system implementing a specific model for associative memory was described by Hopfield [1]. A circuit model for this operation is illustrated in Figure 1, and consists of three major components. A collection of active gain elements (called amplifiers or "neurons") with gain function V = g(v) are connected by a passive interconnect matrix which provides unidirectional excitatory or inhibitory connections ("synapses") between the output of one neuron and the input to another. The strength of this interconnection is given by the
conductance G_(ij) = G_0T_(ij). The requirements placed on the gain function g(v) are not very severe [2], and easily met by VLSI-realizable amplifiers. The third circuit element is the capacitances that determine the time evolution of the system, and are modelled as lumped capacitances.
This formulation leads to the equations of motion shown in Figure 2, and to a Liapunov energy function which determines the dynamics of the system, and predicts the location of stable states (memories) in the case of a symmetric matrix T.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/5ghjd-stc46Real-Time Visual Computations Using Analog CMOS Processing Arrays
https://resolver.caltech.edu/CaltechAUTHORS:20150203-152114654
Authors: {'items': [{'id': 'Sivilotti-M-A', 'name': {'family': 'Sivilotti', 'given': 'Massimo A.'}}, {'id': 'Mahowald-M-A', 'name': {'family': 'Mahowald', 'given': 'Michelle A.'}}, {'id': 'Mead-C-A', 'name': {'family': 'Mead', 'given': 'Carver A.'}}]}
Year: 1987
Integration of photosensors and processing elements provides a mechanism to concurrently perform computations previously intractable in real-time. We have used this approach to model biological early vision
processes. A set of VLSI "retina" chips have been fabricated, using large scale analog circuits (over lOOK transistors in total). Analog processing provides sophisticated, compact functional elements,
and avoids some of the aliasing problems encountered in conventional sampled-data artificial vision systems.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/ds6xq-52n71A Novel Associative Memory Implemented Using Collective Computation
https://resolver.caltech.edu/CaltechAUTHORS:20141223-104942966
Authors: {'items': [{'id': 'Sivilotti-M-A', 'name': {'family': 'Sivilotti', 'given': 'Massimo A.'}}, {'id': 'Emerling-M', 'name': {'family': 'Emerling', 'given': 'Michael'}}, {'id': 'Mead-C-A', 'name': {'family': 'Mead', 'given': 'Carver'}}]}
Year: 1990
A radically new type of associative memory, the ASSOCMEM, has been implemented in VLSI and tested. Analog circuit techniques are used to construct a network that evolves towards fully restored (digital) fixed-points that are the memories of the system. Association occurs
on the whole source word, each bit of which may assume a continuous analog value. The network does not require the distinction of a search key from a data field in either the source or target words. A key may be dynamically defined by differentially weighting any subset of the source
word. The key need not be exact; the system will evolve to the closest memory. In the case when the key is the whole input word, the system may be thought of as performing error correction.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/a83md-ztr69