Cai et al., 2019 - Google Patents
Low bit-width convolutional neural network on RRAMCai et al., 2019
View PDF- Document ID
- 15622940568643809729
- Author
- Cai Y
- Tang T
- Xia L
- Li B
- Wang Y
- Yang H
- Publication year
- Publication venue
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
External Links
Snippet
The emerging resistive random-access memory (RRAM) has been widely applied in accelerating the computing of deep neural networks. However, it is challenging to achieve highprecision computations based on RRAM due to the limits of the resistance level and the …
- 230000001537 neural 0 title abstract description 43
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- 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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- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
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- 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
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