Nvidia corporation (20250103049). QUANTIZING AUTOENCODERS IN A NEURAL NETWORK
QUANTIZING AUTOENCODERS IN A NEURAL NETWORK
Organization Name
Inventor(s)
Jon Hasselgren of Bunkeflostrand SE
QUANTIZING AUTOENCODERS IN A NEURAL NETWORK
This abstract first appeared for US patent application 20250103049 titled 'QUANTIZING AUTOENCODERS IN A NEURAL NETWORK
Original Abstract Submitted
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.