Quantizing autoencoders in a neural network
AssigneeNVIDIA Corporation
InventorsJon Hasselgren, Jacob Munkberg
Life of this patent
Estimating the term…
Abstract
The performance of a neural network is improved by applying quantization to data at various points in the network. In an embodiment, a neural network includes two paths. A quantization is applied to each path, such that when an output from each path is combined, further quantization is not required. In an embodiment, the neural network is an autoencoder that includes at least one skip connection. In an embodiment, the system determines a set of quantization parameters based on the characteristics of the data in the primary path and in the skip connection, such that both network paths produce output data in the same fixed point format. As a result, the data from both network paths can be combined without requiring an additional quantization.
Claims20
A computer-implemented method, comprising:
- identifying a first range of values input to a first path of one or more neural networks;
- identifying a second range of values input to a second path of the one or more neural networks;
- selecting, based at least in part on the first range of values and the second range of values, a set of quantization parameters to be used by both of the first path of the one or more neural networks, and the second path of the one or more neural networks;
- and
- computing output of the first path of the one or more neural networks and the second path of the one or more neural networks based, at least in part, on the selected set of quantization parameters.
A system, comprising:
- one or more processors to respond to one or more application programming interface (API) calls by at least causing a parallel processing unit to:
- identify a first range of values to be input to a first path of one or more neural networks and a second range of values to be input to a second path of the one or more neural networks;
- determine, based at least in part on the first range of values and the second range of values, a set of quantization parameters to be shared between the first path of the one or more neural networks and the second path of the one or more neural networks;
- and
- cause output of the first path of the one or more neural networks and the second path of the one or more neural networks to both be computed using the shared set of quantization parameters.
One or more processors, comprising:
- circuitry to:
- identify a first range of values input to a first path of one or more neural networks;
- identify a second range of values input to a second path of the one or more neural networks;
- select, based at least in part on the first range of values and the second range of values, a set of quantization parameters to be shared between the first path of the one or more neural networks and the second path of the one or more neural networks;
- and
- cause output of the first path of the one or more neural networks and the second path of the one or more neural networks to both be computed using the selected shared set of quantization parameters.
Dependencies are inferred from the claim text.