Compressing information in an end node using an autoencoder neural network
AssigneeSilicon Laboratories Inc.
InventorsANTONIO TORRINI, JAVIER ELENES
Life of this patent
Estimating the term…
Abstract
In one embodiment, an apparatus includes: a sensor to sense real world information; a digitizer coupled to the sensor to digitize the real world information into digitized information; a signal processor coupled to the digitizer to process the digitized information into a spectrogram; a neural engine coupled to the signal processor, the neural engine comprising an autoencoder to compress the spectrogram into a compressed spectrogram; and a wireless circuit coupled to the neural engine to send the compressed spectrogram to a remote destination, to enable the remote destination to process the compressed spectrogram.
Claims20
At least one computer readable storage medium having stored thereon instructions, which if performed by a machine cause the machine to perform a method comprising:
- generating an autoencoder comprising an encoder and a decoder, and generating a classifier, wherein the encoder is to encode a spectrogram into a compressed spectrogram, the decoder is to decode the compressed spectrogram into a recovered spectrogram, and the classifier is to identify one or more properties of real world information from the recovered spectrogram;
- calculating a first loss of the autoencoder and calculating a second loss of the classifier;
- jointly training the autoencoder and the classifier based at least in part on the first loss and the second loss;
- and
- storing the trained autoencoder and the trained classifier in a non-transitory storage medium.
A method comprising:
- generating, in a computer system, an autoencoder comprising an encoder and a decoder, and generating a classifier, wherein the encoder is to encode a spectrogram into a compressed spectrogram, the decoder is to decode the compressed spectrogram into a recovered spectrogram, and the classifier is to identify one or more properties of real world information from the recovered spectrogram;
- calculating, in the computer system, a first loss of the autoencoder and calculating a second loss of the classifier;
- jointly training, in the computer system, the autoencoder and the classifier based at least in part on the first loss and the second loss;
- and
- storing the trained autoencoder and the trained classifier in a non-transitory storage medium.
A system comprising:
- at least one processor;
- memory coupled to the at least one processor;
- and
- one or more non-transitory storage media, wherein the one or more non-transitory storage media comprises instructions which if performed by the system cause the system to perform a method comprising:
- generating an autoencoder comprising an encoder and a decoder, and generating a classifier, wherein the encoder is to encode a spectrogram into a compressed spectrogram, the decoder is to decode the compressed spectrogram into a recovered spectrogram, and the classifier is to identify one or more properties of real world information from the recovered spectrogram;
- calculating a first loss of the autoencoder and calculating a second loss of the classifier;
- jointly training the autoencoder and the classifier based at least in part on the first loss and the second loss;
- and
- storing the trained autoencoder and the trained classifier in the one or more non-transitory storage media.
Dependencies are inferred from the claim text.