Texas Instruments Corporation

Intel Corporation

Dipan Kumar Mandal

Om Ji Omer

Jayasree Sankaranarayanan

Bryan Joseph Thome

Gurpreet Singh Kalsi

Mihir Narendra Mody

Niraj Nandan

Sreenivas Subramoney

Hetul Sanghvi

Mahesh Madhukar Mehendale

Gary A Cooper

Pavan Venkata Shastry

Subrangshu Kumar Das

Gopi Neela

Kanika Ghai Bansal

Biji George

Anirud Thyagharajan

Prashant Laddha

Nainala Vyagrheswarudu

Vijayavardhan Baireddy

Santhosh Kumar Rethinagiri

Soyeb Noormohammed Nagori

Yuval Amizur

Leor Banin

Belliappa Manavattira Kuttanna

Hrushikesh Tukaram Garud

Lance E Hacking

Nir Dvorecki

Prashanth Ramanathpur Viswanath

Cormac Brick

Shengbo Xu

Chaitanya Satish Ghone

Chulong Chen

Piyali Goswami

Saiveena Kesaraju

Mahesh Mamidipaka

Pradyumna S Singh

Sanmati S Kamath

Nagadastagiri Reddy C

Brian Joseph Thome

Wenling Margaret Huang

Srivatsava Jandhyala

Anish N K

Naresh Kumar Yadav

Wonki Choi

Ivan Simões Gaspar

Hao Chen

According to various embodiments, a radar device is described comprising a processor configured to generate a scene comprising an object based on a plurality of receive wireless signals, generate a ground truth object parameter of the object and generate a dataset representative of the scene and a radar detector configured to determine an object parameter of the object using a machine learning algorithm and the dataset, determine an error value of the machine learning algorithm using a cost function, the object parameter, and the ground truth object parameter and adjust the machine learning algorithm values to reduce the error value.

High end imaging radar

This disclosure is directed to the problem of paralleling random read access within a reasonably sized block of data for a vector SIMD processor. The invention sets up plural parallel look up tables, moves data from main memory to each plural parallel look up table and then employs a look up table read instruction to simultaneously move data from each parallel look up table to a corresponding part a vector destination register. This enables data processing by vector single instruction multiple data (SIMD) operations. This vector destination register load can be repeated if the tables store more used data. New data can be loaded into the original tables if appropriate. A level one memory is preferably partitioned as part data cache and part directly addressable memory. The look up table memory is stored in the directly addressable memory.

Method for performing random read access to a block of data using parallel lut read instruction in vector processors

Method for performing random read access to a block of data using parallel LUT read instruction in vector processors

A video hardware engine which support dynamic frame padding is disclosed. The video hardware engine includes an external memory. The external memory stores a reference frame. The reference frame includes a plurality of reference pixels. A motion estimation (ME) engine receives a current LCU (largest coding unit), and defines a search area around the current LCU for motion estimation. The ME engine receives a set of reference pixels corresponding to the current LCU. The set of reference pixels of the plurality of reference pixels are received from the external memory. The ME engine pads a set of duplicate pixels along an edge of the reference frame when a part area of the search area is outside the reference frame.

Dynamic frame padding in a video hardware engine

A disclosed apparatus to multiply matrices includes a compute engine. The compute engine includes multipliers in a two dimensional array that has a plurality of array locations defined by columns and rows. The apparatus also includes a plurality of adders in columns. A broadcast interconnect between a cache and the multipliers broadcasts a first set of operand data elements to multipliers in the rows of the array. A unicast interconnect unicasts a second set of operands between a data buffer and the multipliers. The multipliers multiply the operands to generate a plurality of outputs, and the adders add the outputs generated by the multipliers.

Methods, apparatus, articles of manufacture to perform accelerated matrix multiplication

An example apparatus for selecting keypoints in image includes a keypoint detector to detect keypoints in a plurality of received images. The apparatus also includes a score calculator to calculate a keypoint score for each of the detected keypoints based on a descriptor score indicating descriptor invariance. The apparatus includes a keypoint selector to select keypoints based on the calculated keypoint scores. The apparatus also further includes a descriptor calculator to calculate descriptors for each of the selected keypoints. The apparatus also includes a descriptor matcher to match corresponding descriptors between images in the plurality of received images. The apparatus further also includes a feature tracker to track a feature in the plurality of images based on the matched descriptors.

Selecting keypoints in images using descriptor scores

An embodiment of an image processor device includes technology to fetch a feature point data set from outside a local memory, locally store three or more fetched feature point data sets in the local memory, compute orientation information for each fetched feature point data set, compute first descriptor information based on the computed orientation information and a first locally stored feature point data set in parallel with a fetch and local store of a second feature point data set in the local memory, and compute second descriptor information based on the computed orientation information and the second locally stored feature point data set in parallel with the compute of the first descriptor information. Other embodiments are disclosed and claimed.

Architecture to generate binary descriptor for image feature point

An example apparatus for tracking features in image data includes an image data receiver to receive initial image data corresponding to an image from a camera and store the image data a circular buffer. The apparatus also includes a feature detector to detect features in the image data. The apparatus further includes a feature sorter to sort the detected features to generate sorted feature points. The apparatus includes a feature tracker to track the sorted feature points in subsequent image data corresponding to the image received at the image data receiver. The subsequent image data is to replace the initial image data in the circular buffer.

Feature detection, sorting, and tracking in images using a circular buffer

An apparatus, method, and computer readable medium to accommodate depth noise in SLAM (Simultaneous Localization & Mapping). The method includes receiving correspondences and clusters for pose estimation. After the correspondences and clusters are received, a dynamic centroid is determined using 3-D features of a landmark. The 3-D features of the landmark comprise a cluster. Next, distance-error metrics are determined using the dynamic centroid, a map point, and the cluster. The distance-error metrics are compared with thresholds to remove depth noise affected 3-D features and landmarks when the distance-error metrics are larger than the thresholds. The remaining 3-D features of the cluster are sent to a pose estimation framework.

Accommodating depth noise in visual slam using map-point consensus

Systems and methods for a hardware accelerated matrix decomposition matrix decomposition circuit are described herein. This matrix decomposition circuit splits matrix decomposition operations into parallel operation circuits and serial operation circuits, and joins the parallel and serial operation circuits using specific dependency handling logic for efficient parallel execution. This provides fast matrix decomposition with low power consumption, reduced memory footprint, and reduced memory bandwidth.

Accelerator for matrix decomposition

Disclosed techniques relate to forming a block sum of picture elements employing a vector dot product instruction to sum packed picture elements and the mask producing a vector of masked horizontal picture element. The block sum is formed from plural horizontal sums via vector single instruction multiple data (SIMD) addition.

Method to compute sliding window block sum using instruction based selective horizontal addition in vector processor

A control processor for a video encode-decode engine is provided that includes an instruction pipeline. The instruction pipeline includes an instruction fetch stage coupled to an instruction memory to fetch instructions, an instruction decoding stage coupled to the instruction fetch stage to receive the fetched instructions, and an execution stage coupled to the instruction decoding stage to receive and execute decoded instructions. The instruction decoding stage and the instruction execution stage are configured to decode and execute a set of instructions in an instruction set of the control processor that are designed specifically for accelerating video sequence encoding and encoded video bit stream decoding.

Processor instructions for accelerating video coding

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Bengaluru, India

Dipan Kumar Mandal