[go: up one dir, main page]

Miller, 2019 - Google Patents

The alchemy of computation: designing with the unknown

Miller, 2019

View HTML
Document ID
17924090363470946134
Author
Miller J
Publication year
Publication venue
Natural Computing

External Links

Snippet

Modern computers allow a methodical search of possibly billions of experiments and the exploitation of interactions that are not known in advance. This enables a bottom-up process of design by assembling or configuring systems and testing the degree to which they fulfill …
Continue reading at link.springer.com (HTML) (other versions)

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
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • 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
    • 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
    • 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
    • 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
    • G06N99/002—Quantum computers, i.e. information processing by using quantum superposition, coherence, decoherence, entanglement, nonlocality, teleportation
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled

Similar Documents

Publication Publication Date Title
Mohseni et al. Ising machines as hardware solvers of combinatorial optimization problems
Yang et al. Transiently chaotic simulated annealing based on intrinsic nonlinearity of memristors for efficient solution of optimization problems
Kiraly et al. An atomic Boltzmann machine capable of self-adaption
Mizrahi et al. Neural-like computing with populations of superparamagnetic basis functions
Sutton et al. Intrinsic optimization using stochastic nanomagnets
Parihar et al. Vertex coloring of graphs via phase dynamics of coupled oscillatory networks
Dale et al. Reservoir computing quality: connectivity and topology
Miller The alchemy of computation: designing with the unknown
Rzeszut et al. Multi-state MRAM cells for hardware neuromorphic computing
Dale et al. Reservoir computing as a model for in-materio computing
Broersma et al. Computational matter: Evolving computational functions in nanoscale materials
Sidler et al. Unsupervised learning using phase-change synapses and complementary patterns
Stepney Physical reservoir computing: a tutorial
Haddow et al. Evolvable hardware challenges: Past, present and the path to a promising future
Saito et al. Amoeba-inspired analog electronic computing system integrating resistance crossbar for solving the travelling salesman problem
Al Beattie et al. Criticality in FitzHugh-Nagumo oscillator ensembles: Design, robustness, and spatial invariance
Zhang et al. A fully-integrated memristor chip for edge learning
Bradley et al. Pattern recognition using spiking antiferromagnetic neurons
Rakos Modeling of dipole–dipole‐coupled, electric field‐driven, protein‐based computing architectures
Miller et al. In materio computation using carbon nanotubes
Greff et al. Using neural networks to predict the functionality of reconfigurable nano-material networks
Patel et al. AI-guided framework for the design of materials and devices for magnetic-tunnel-junction-based true random number generators
Mountain et al. Ohmic weave: Memristor-based threshold gate networks
Vissol-Gaudin et al. Data classification using carbon-nanotubes and evolutionary algorithms
Lawrence Evolving networks to have intelligence realized at nanoscale