Zhang et al., 2023 - Google Patents
HyperSpikeASIC: Accelerating Event-Based Workloads With HyperDimensional Computing and Spiking Neural NetworksZhang et al., 2023
View PDF- Document ID
- 16040648546046804056
- Author
- Zhang T
- Morris J
- Stewart K
- Lui H
- Khaleghi B
- Thomas A
- Goncalves-Marback T
- Aksanli B
- Neftci E
- Rosing T
- Publication year
- Publication venue
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
External Links
Snippet
Today's machine learning (ML) systems, running workloads, such as deep neural networks, which require billions of parameters and many hours to train a model, consume a significant amount of energy. Due to the complexity of computation and topology, even the quantized …
- 238000012421 spiking 0 title abstract description 18
Classifications
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- 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
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- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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- G06K9/627—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns
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