Mobileye Vision Technologies Ltd.

Daniel Srebnik

Gil Israel Dogon

Emmanuel Sixou

Yan Dubinski

Dror Livne

A method of calculating warp results, the method may include executing, for each target pixel out of a group of target pixels, a warp calculation process that comprises: receiving, by a first group of processing units of an array of processing units, a first weight and a second weight associated with the target pixel; receiving, by a second group of processing units of the array, values of neighboring source pixels associated with the target pixel; calculating, by the second group, a warp result based on it response to values of the neighboring source pixels and the pair of weights; and providing the warp result to a memory module.

Image processing array with multi-path relay channel for relaying data between relay ports of data processors

A method for on-the fly updating of a processing circuit, the method includes monitoring, by multiple coroutines and during a monitoring period, a progress of multiple suspend-update-resume sequences executed by the processing circuit, wherein at least some of the multiple execute and suspend-update-resume sequences partially overlap and are not mutually synchronized, and wherein each suspend-update-resume sequence comprises on-the-fly updates; and determining, by a merged coroutine, timings of the multiple suspend-update-resume sequences, wherein the determining comprises performing multiple calculation iterations, wherein a calculation iteration of the multiple calculation iterations comprises calculating, in a an iterative manner, a timing of a next suspend-update-resume sequence to be executed out of the multiple suspend-update-resume sequences, and wherein the calculating is responsive to timing offsets between different suspend-update-resume sequences.

On the fly configuration of a processing circuit

A method for configuring a processing circuit, the method may include (i) receiving, by the processing circuit, a compressed configuration information data structure (CCDS) that comprises multiple segments, wherein the CCDS was generated by a size-preserving compression process that maintains a size of the segments; (ii) decompressing the CCDS, by the processing circuit, to provide decompressed configuration information, wherein the decompressing comprises: searching for headers, wherein a header comprises sequence parameters, wherein the sequence parameters comprise at least one out of a length, an address field, and a type; and (iii) configuring the processing circuit using the decompressed configuration information, wherein the configuring is executed based on the headers.

Fast configuration of a processing circuit

There may be provided a non-uniform Benes network, that may include a first Benes network portion that has a first number (k) of first inputs and k first outputs; a second Benes network portion that has a second number (j) of second inputs and j second outputs; wherein j is smaller than k; and a set of multiplexers that are coupled between a set of switches of an intermediate layer of the first Benes network portion and a first layer of the second Benes network layer.

Image processor and methods for processing an image

A method of calculating warp results for at least one out of driver assistance and autonomous driving, the method may include executing, for each target pixel out of a group of target pixels, a warp calculation process that includes: (a) Receiving, by a first group of processing units of an array of processing units, a first weight and a second weight associated with the target pixel. (b) Receiving, by a second group of processing units of the array, values of neighboring source pixels associated with the target pixel (c) Calculating, by the second group, a warp result based on in response to values of the neighboring source pixels and the pair of weights (d) And providing the warp result to a memory module.

Array of processing units of an image processor and methods for calculating a warp result

Parallel processing of an image using an array of addressable registers. Image features are extracted from the image. The image features are storable as data. According to respective values of a sorting key derived from a parameter of the data, the image features are sorted into N buckets. Using an array of M addressable registers, where M is less than N, the data are summed within the buckets to perform a histogram of the image features, according to values of a histogram key derived from said a parameter of the data.

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Jerusalem, Israel

Daniel Srebnik