Kothapalli, 1995 - Google Patents
Artificial neural networks as aids in circuit designKothapalli, 1995
- Document ID
- 3919292897776969207
- Author
- Kothapalli G
- Publication year
- Publication venue
- Microelectronics Journal
External Links
Snippet
This paper introduces the application of software implementations of artificial neural networks in the design of microelectronic circuits. The device dimensions of CMOS transistors can be easily obtained from trained artificial neural networks. Data generated by …
- 230000001537 neural 0 title abstract description 50
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
- 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
- G06N3/04—Architectures, e.g. interconnection topology
- G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
-
- 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
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
- G06F17/5036—Computer-aided design using simulation for analog modelling, e.g. for circuits, spice programme, direct methods, relaxation methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US6687686B1 (en) | Hebbian synapse circuit | |
US4903226A (en) | Switched networks | |
Dualibe et al. | Design of analog fuzzy logic controllers in CMOS technologies: implementation, test and application | |
Huayaney et al. | Learning in silicon beyond STDP: a neuromorphic implementation of multi-factor synaptic plasticity with calcium-based dynamics | |
Foo et al. | Analog components for the VLSI of neural networks | |
Simoni et al. | Adaptation in a VLSI model of a neuron | |
Kothapalli | Artificial neural networks as aids in circuit design | |
Hammouda et al. | Neural-based models of semiconductor devices for SPICE simulator | |
Bibyk et al. | Issues in analog VLSI and MOS techniques for neural computing | |
Kahraman et al. | Technology independent circuit sizing for fundamental analog circuits using artificial neural networks | |
Caviglia et al. | Effects of weight discretization on the back propagation learning method: Algorithm design and hardware realization | |
Hirasawa et al. | Chaos control on universal learning networks | |
Long et al. | Memristive-synapse spiking neural networks based on single-electron transistors | |
US6513023B1 (en) | Artificial neural network with hardware training and hardware refresh | |
Meador et al. | A low-power CMOS circuit which emulates temporal electrical properties of neurons | |
Avci et al. | Neural network based MOS transistor geometry decision for TSMC 0.18 μ process technology | |
Shah et al. | Aspect ratio estimation for MOS amplifier using machine learning | |
Salam | Learning Algorithms for Artifical Neural Nets for Analog Circuit Implementation | |
Wang et al. | A novel nonlinear control for uncertain polynomial type-2 fuzzy systems (case study: cart-Pole system) | |
Chan et al. | A computation-efficient on-line training algorithm for neurofuzzy networks | |
Oliveira Weber et al. | Topology variations of an amplifier-based mos analog neural network implementation and weights optimization | |
Oh et al. | Analog CMOS implementation of neural network for adaptive signal processing | |
Avcı et al. | Neural network based transistor modeling and aspect ratio estimation for yital 1.5 micron process | |
Grosu | ResNets, NeuralODEs and CT-RNNs are Particular Neural Regulatory Networks | |
Hasan et al. | Fast Simulation of Analog Spiking Neural Network With Device Non-Idealites |