Intel Corporation

Chyuan-Tyng Wu

Gilad Michael

Furkan Isikdogan

Bhavin V Nayak

Joao Peralta Moreira

Sushma Rao

Paul S Diefenbaugh

Kunjal ShyamKumar Parikh

Vishal Ravindra Sinha

Liron Ain-Kedem

Shravan Kumar Belagal Math

Jun Nishimura

Timo Gerasimow

Aleksandar Sutic

Chaitanya R Gandra

Avi Kalderon

Pavel Kounitsky

Leo Isikdogan

Malhar N Bhatt

Ben Berlin

Masayoshi Asama

Methods, apparatus, systems, and articles of manufacture to control an aggressiveness of display panel power savings are disclosed. An example apparatus include a display panel, a display controller to adjust an image to be displayed by the display panel, and a power savings controller to access an image provided to the display controller, execute a machine learning model using the image as an input to generate an aggressiveness value, and provide the aggressiveness value to the display controller, the display controller to adjust the image based on the aggressiveness value.

Methods and apparatus to control an aggressiveness of display panel power savings and fidelity

An apparatus to facilitate partially-frozen neural networks for efficient computer vision systems is disclosed. The apparatus includes a frozen core to store fixed weights of a machine learning model, one or more trainable cores coupled to the frozen core, the one or more trainable cores comprising multipliers for trainable weights of the machine learning model, and wherein the alpha blending layer includes a trainable alpha blending parameter, and wherein the trainable alpha blending parameter is a function of a trainable parameter, a sigmoid function, and outputs of frozen and trainable blocks in a preceding layer of the machine learning model.

Partially-frozen neural networks for efficient computer vision systems

An example apparatus for processing images includes a trainable vision scaler to receive an image. The trainable vision scaler is to generate output including a feature map or an enhanced image based on the image. The trainable vision scaler is to transmit the output to a computer vision network. The computer vision network is trained to perform a computer vision task using the output.

Trainable vision scaler

An example apparatus for processing images includes a hybrid infinite impulse response-finite impulse response (IIR-FIR) convolution block to receive an image and generate processed image information. The hybrid IIR-FIR convolution block includes a vertical infinite impulse response (IIR) component to approximate a vertical convolution when processing the image.

Processing images using hybrid infinite impulse response (TTR) and finite impulse response (FIR) convolution block

A mechanism is described for facilitating automatic tuning of image signal processors using reference images in image processing environments, according to one embodiment. A method of embodiments, as described herein, includes one or more processors to: receive images associated with one or more scenes captured by one or more cameras; access tuning parameters associated with functionalities within an image signal processor (ISP) pipeline; generate reference images based on the tuning parameters, wherein a reference image is associated with an image for each functionality within the ISP pipeline; and automatically tune the ISP pipeline based on selection of one or more of the reference images for one or more of the images for one or more of the functionalities.

Automatic tuning of image signal processors using reference images in image processing environments

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San Jose, CA, United States of America

Chyuan-Tyng Wu