20,588 research outputs found
An analog feedback associative memory
A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is developed for the Hopfield continuous-time network. An important requirement is that each memory vector has to be an asymptotically stable (i.e. attractive) equilibrium of the network. Some of the limitations imposed by the continuous Hopfield model on the set of vectors that can be stored are pointed out. These limitations can be relieved by choosing a network containing visible as well as hidden units. An architecture consisting of several hidden layers and a visible layer, connected in a circular fashion, is considered. It is proved that the two-layer case is guaranteed to store any number of given analog vectors provided their number does not exceed 1 + the number of neurons in the hidden layer. A learning algorithm that correctly adjusts the locations of the equilibria and guarantees their asymptotic stability is developed. Simulation results confirm the effectiveness of the approach
Current-Mode Techniques for the Implementation of Continuous- and Discrete-Time Cellular Neural Networks
This paper presents a unified, comprehensive approach
to the design of continuous-time (CT) and discrete-time
(DT) cellular neural networks (CNN) using CMOS current-mode
analog techniques. The net input signals are currents instead
of voltages as presented in previous approaches, thus avoiding
the need for current-to-voltage dedicated interfaces in image
processing tasks with photosensor devices. Outputs may be either
currents or voltages. Cell design relies on exploitation of current
mirror properties for the efficient implementation of both linear
and nonlinear analog operators. These cells are simpler and
easier to design than those found in previously reported CT
and DT-CNN devices. Basic design issues are covered, together
with discussions on the influence of nonidealities and advanced
circuit design issues as well as design for manufacturability
considerations associated with statistical analysis. Three prototypes
have been designed for l.6-pm n-well CMOS technologies.
One is discrete-time and can be reconfigured via local logic for
noise removal, feature extraction (borders and edges), shadow
detection, hole filling, and connected component detection (CCD)
on a rectangular grid with unity neighborhood radius. The other
two prototypes are continuous-time and fixed template: one for
CCD and other for noise removal. Experimental results are given
illustrating performance of these prototypes
The Resilience of Computationalism
Computationalismâthe view that cognition is computationâhas always been controversial. It faces two types of objection. According to insufficiency objections, computation is insufficient for some cognitive phenomenon X. According to objections from neural realization, cognitive processes are realized by neural processes, but neural processes have feature Y and having Y is incompatible with being (or realizing) computations. In this paper, I explain why computationalism has survived these objections. Insufficiency objections are at best partial: for all they establish, computation may be sufficient for cognitive phenomena other than X, may be part of the explanation for X, or both. Objections from neural realization are based either on a false contrast between feature Y and computation or on an account of computation that is too vague to yield the desired conclusion. To adjudicate the dispute between computationalism and its foes, I will conclude that we need a better account of computation
Analog Neural Programmable Optimizers in CMOS VLSI Technologies
A 3-ÎŒm CMOS IC is presented demonstrating the concept of an analog neural system for constrained optimization. A serial time-multiplexed general-purpose architecture is introduced for the real-time solution of this kind of problem in MOS VLSI. This architecture is a fully programmable and reconfigurable one exploiting SC techniques for the analog part and making extensive use of digital techniques for programmability
Analog readout for optical reservoir computers
Reservoir computing is a new, powerful and flexible machine learning
technique that is easily implemented in hardware. Recently, by using a
time-multiplexed architecture, hardware reservoir computers have reached
performance comparable to digital implementations. Operating speeds allowing
for real time information operation have been reached using optoelectronic
systems. At present the main performance bottleneck is the readout layer which
uses slow, digital postprocessing. We have designed an analog readout suitable
for time-multiplexed optoelectronic reservoir computers, capable of working in
real time. The readout has been built and tested experimentally on a standard
benchmark task. Its performance is better than non-reservoir methods, with
ample room for further improvement. The present work thereby overcomes one of
the major limitations for the future development of hardware reservoir
computers.Comment: to appear in NIPS 201
- âŠ