Du et al., 2019 - Google Patents
Neural network circuits and parallel implementationsDu et al., 2019
- Document ID
- 3851100799252297166
- Author
- Du K
- Swamy M
- Du K
- Swamy M
- Publication year
- Publication venue
- Neural Networks and Statistical Learning
External Links
Snippet
Hardware and parallel implementations can substantially speed up machine learning algorithms to extend their widespread applications. In this chapter, we first introduce various circuit realizations for popular neural network learning methods. We then introduce their …
- 230000001537 neural 0 title abstract description 123
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
-
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/76—Architectures of general purpose stored programme computers
- G06F15/78—Architectures of general purpose stored programme computers comprising a single central processing unit
-
- 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
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- 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
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
- G06F15/163—Interprocessor communication
- G06F15/173—Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star, snowflake
-
- 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
- G06F7/38—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation
- G06F7/48—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state device; using unspecified devices
- G06F7/52—Multiplying; Dividing
- G06F7/523—Multiplying only
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Schuman et al. | Opportunities for neuromorphic computing algorithms and applications | |
Wu et al. | Brain-inspired global-local learning incorporated with neuromorphic computing | |
Thakur et al. | Large-scale neuromorphic spiking array processors: A quest to mimic the brain | |
Bouvier et al. | Spiking neural networks hardware implementations and challenges: A survey | |
Jawandhiya | Hardware design for machine learning | |
Calimera et al. | The human brain project and neuromorphic computing | |
Misra et al. | Artificial neural networks in hardware: A survey of two decades of progress | |
Yadav et al. | An introduction to neural network methods for differential equations | |
Wang et al. | Liquid state machine based pattern recognition on FPGA with firing-activity dependent power gating and approximate computing | |
Wang et al. | Towards ultra-high performance and energy efficiency of deep learning systems: an algorithm-hardware co-optimization framework | |
Altaisky et al. | Quantum neural networks: Current status and prospects for development | |
Petersen et al. | Deep differentiable logic gate networks | |
Xu et al. | Accelerating dynamic time warping with memristor-based customized fabrics | |
Cardwell et al. | Truly heterogeneous hpc: Co-design to achieve what science needs from hpc | |
Valencia et al. | An artificial neural network processor with a custom instruction set architecture for embedded applications | |
Chen et al. | Emat: an efficient multi-task architecture for transfer learning using reram | |
Du et al. | Neural network circuits and parallel implementations | |
Carpegna et al. | Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge | |
Hanif et al. | Resistive crossbar-aware neural network design and optimization | |
Ahmed et al. | Brain-inspired spiking neural networks | |
Lawrence et al. | Matrix multiplication by neuromorphic computing | |
Shen et al. | Cortical columns computing systems: Microarchitecture model, functional building blocks, and design tools | |
Ahmed et al. | System design for in-hardware stdp learning and spiking based probablistic inference | |
Bodiwala et al. | An efficient stochastic computing based deep neural network accelerator with optimized activation functions | |
Du et al. | Neural Circuits and Parallel Implementation |