Vittoz, 1994 - Google Patents
Analog VLSI signal processing: Why, where, and how?Vittoz, 1994
View PDF- Document ID
- 3360958774734069709
- Author
- Vittoz E
- Publication year
- Publication venue
- Analog Integrated Circuits and Signal Processing
External Links
Snippet
Analog VLSI signal processing is most effective when precision is not required, and is therefore an ideal solution for the implementation of perception systems. The possibility to choose the physical variable that represents each signal allows all the features of the …
- 238000003860 storage 0 abstract description 20
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
- G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
- G05F3/00—Non-retroactive systems for regulating electric variables by using an uncontrolled element, or an uncontrolled combination of elements, such element or such combination having self-regulating properties
- G05F3/02—Regulating voltage or current
- G05F3/08—Regulating voltage or current wherein the variable is dc
- G05F3/10—Regulating voltage or current wherein the variable is dc using uncontrolled devices with non-linear characteristics
- G05F3/16—Regulating voltage or current wherein the variable is dc using uncontrolled devices with non-linear characteristics being semiconductor devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06G—ANALOGUE COMPUTERS
- G06G7/00—Devices in which the computing operation is performed by varying electric or magnetic quantities
- G06G7/12—Arrangements for performing computing operations, e.g. operational amplifiers
- G06G7/18—Arrangements for performing computing operations, e.g. operational amplifiers for integration or differentiation; for forming integrals
- G06G7/184—Arrangements for performing computing operations, e.g. operational amplifiers for integration or differentiation; for forming integrals using capacitative elements
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Vittoz | Analog VLSI signal processing: Why, where, and how? | |
Vittoz | Analog VLSI implementation of neural networks | |
Andreou et al. | Translinear circuits in subthreshold MOS | |
US5959871A (en) | Programmable analog array circuit | |
US6744299B2 (en) | Electronic array having nodes and methods | |
Verleysen et al. | An analog VLSI implementation of Hopfield's neural network | |
Schneider et al. | Analog CMOS deterministic Boltzmann circuits | |
Verleysen et al. | Neural networks for high-storage content-addressable memory: VLSI circuit and learning algorithm | |
Vittoz | Pseudo-resistive networks and their applications to analog collective computation | |
US4961005A (en) | Programmable neural circuit implementable in CMOS very large scale integration | |
Marshall et al. | Fuzzy logic architecture using subthreshold analogue floating-gate devices | |
Dominguez-Castro et al. | Four-quadrant one-transistor-synapse for high-density CNN implementations | |
Li et al. | A compact current mode neuron circuit with Gaussian taper learning capability | |
Vidal-Verdú et al. | Using building blocks to design analog neuro-fuzzy controllers | |
Vidal-Verdu et al. | A basic building block approach to CMOS design of analog neuro/fuzzy systems | |
Lubkin et al. | VLSI implementation of fuzzy adaptive resonance and learning vector quantization | |
Li et al. | A 0.7 v low-power fully programmable gaussian function generator for brain-inspired gaussian correlation associative memory | |
Dualibel et al. | On designing mixed-signal programmable fuzzy logic controllers as embedded subsystems in standard CMOS technologies | |
Rodríguez Vázquez et al. | Accurate design of analog CNN in CMOS digital technologies | |
Heim et al. | Precise analog synapse for Kohonen feature maps | |
Djahanshahi et al. | A unified synapse-neuron building block for hybrid VLSI neural networks | |
US20040083193A1 (en) | Expandable on-chip back propagation learning neural network with 4-neuron 16-synapse | |
Peng et al. | A programmable floating-gate bump circuit with variable width | |
Landolt | An analog CMOS implementation of a Kohonen network with learning capability | |
Ahmed et al. | Hierarchical Analog Behavioral Modeling of Artificial Neural Networks |