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Zhang et al., 2023 - Google Patents

HyperSpikeASIC: Accelerating Event-Based Workloads With HyperDimensional Computing and Spiking Neural Networks

Zhang et al., 2023

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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 …
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Classifications

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    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
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    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
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    • G06COMPUTING; CALCULATING; COUNTING
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    • G06K9/627Classification 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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