Verint Systems, Inc.

Intel Corporation

Jeremie Dreyfuss

Tomer Bar-On

Yaniv Fais

Lev Faivishevsky

Jacob Subag

Omer Ziv

Ido Shapira

Ran Achituv

Amit Bleiweiss

Tomer Schwartz

Gal Leibovich

Amitai Armon

Uzi Sarel

Michael Behar

Guy Jacob

Ehud Cohen

Itamar Ben-Ari

Yahav Shadmiy

Yair Horesh

Roni Romano

Galina Ryvchin

Eran Ben-Avi

Neta Zmora

Eyal Yaacoby

Raanan Yonatan Yehezkel Rohekar

Gal Novik

Zigi Walter

Orly Weisel

Shira Hirsch

Yarden Oren

Justin E Gottschlich

Niranjan Hasabnis

Oren David Kimhi

Amital Armon

In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.

Real time context dependent deep learning

In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer. Other embodiments are also disclosed and claimed.

Low rank matrix compression

In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Scheduling configuration for deep learning networks

A method of zoning a transcription of audio data includes separating the transcription of audio data into a plurality of utterances. A that each word in an utterances is a meaning unit boundary is calculated. The utterance is split into two new utterances at a work with a maximum calculated probability. At least one of the two new utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.

System and method of text zoning

In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to expose embedded cast operations in at least one of a load instruction or a store instruction; determine a target precision level for the cast operations; and load the cast operations at the target precision level. Other embodiments are also disclosed and claimed.

Variable precision and mix type representation of multiple layers in a network

Systems and method of diarization of audio files use an acoustic voiceprint model. A plurality of audio files are analyzed to arrive at an acoustic voiceprint model associated to an identified speaker. Metadata associate with an audio file is used to select an acoustic voiceprint model. The selected acoustic voiceprint model is applied in a diarization to identify audio data of the identified speaker.

System and method of video capture and search optimization for creating an acoustic voiceprint

Methods, apparatus, systems, and articles of manufacture are disclosed to detect code defects. An example apparatus includes repository interface circuitry to retrieve code repositories corresponding to a programming language of interest, tree generating circuitry to generate parse trees corresponding to code blocks contained in the code repositories, directed acyclic graph (DAG) circuitry to generate DAGs corresponding to respective ones of the parse trees, the DAGs including control flow information and data flow information, abstraction generating circuitry to abstract the DAGs, invariant identification circuitry to extract invariants from the abstracted DAGs, and DAG comparison circuitry to cluster respective ones of the extracted invariants to identify respective ones of the abstracted DAGs with common invariants.

Methods, systems, articles of manufacture and apparatus to detect code defects

In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

Storage system of DNN outputs for black box

In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Multiply-accumulate '0' data gating

In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer.

In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Sub-graph in frequency domain and dynamic selection of convolution implementation on a GPU

Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

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Tel Aviv, Israel

Jeremie Dreyfuss