Digital integrated circuit (IC) for extracting features out of input image is disclosed. The IC
contains one or more identical cellular neural networks (CNN) processing engines …

Embodiments of the present disclosure relate to a configurable convolution engine that
receives configuration information to perform convolution or its variant operations on …

Embodiments relate to a configurable convolution engine that receives configuration
information to perform convolution and other deep machine learning operations on …

CNN based integrated circuit is configured with a set of pre-trained filter coefficients or
weights as a feature extractor of an input data. Multiple fully-connected networks (FCNs) are …

Embodiments relate to a neural processor circuit including a plurality of neural engine
circuits, a data buffer, and a kernel fetcher circuit. At least one of the neural engine circuits is …

Method and systems of replacing operations of depthwise separable filters with first and
second replacement convolutional layers are disclosed. Depthwise separable filters …

Data arrangement schemes of imagery data and filter coefficients stored in a CNN based
digital IC for extracting features out of an input image are disclosed. The CNN based digital …

Operations of a combination of first and second original convolutional layers followed by a
short path are replaced by operations of a set of three particular convolutional layers. The …

A configurable neuro-inspired convolution processor is designed as an array of neurons
each operating in an independent clock domain. The processor implements a recurrent …

Multiple 3× 3 convolutional filter kernels are used for approximating operations of fully-
connected (FC) layers. Image classification task is entirely performed within a CNN based …