Abstract: Methods and apparatuses for decoding a compressed image using a neural image compression network are provided. The method may include generating context parameters associated with a compressed image, the context parameters corresponding to a first area. The method may also include determining that a long range global dependency exists between a first latent and a second latent in a long-range global area within the compressed image and predicting a plurality of context features using a transformer-based long-range context prediction model. The method may then include reconstructing the compressed image based on the predicted plurality of context features.

Abstract: Described is a system performing operations comprising deriving, based on an evaluation of variations of a first machine learning model, a second machine learning model, the second machine learning model having layers with precisions assigned based on the evaluation, training the second machine learning model to reduce error between a first test output generated by the first machine learning model based on a first test input and a second test output generated by the second machine learning model based on the first test input, training the second machine learning model to reduce error between a third test output generated by the second machine learning model based on a second test input and a ground truth output associated with the second test input, providing an input for the second machine learning model, and generating an output using the second machine learning model based on the input.

Abstract: Methods and apparatuses for decoding a compressed image using a neural image compression network may be provided. The method may include generating long-range context model parameters associated with a high resolution compressed image, the long-range context model parameters corresponding to a first area. The method may also include splitting the generated long-range context model parameters into a first number of context parameter blocks. The method may also include for each block in the first number of context parameter blocks, predicting respective context features using a long-range context model and respective context parameter blocks, wherein the long-range context model uses a corner-to-center latent decoding strategy or an edge-to-center latent decoding strategy to decode latents associated with the high resolution compressed image. Then, the high resolution compressed image may be reconstructed based on predicted context features.

Abstract: Methods and apparatuses for neural network based image compression may be provided. The method may include generating a feature map comprising a plurality of channels for a compressed image; splitting the generated feature map into a first number of split feature maps; and reconstructing the compressed image using one or more long-range attention models with one or more variable receptive fields associated with a respective split feature map.

Abstract: Methods and apparatuses for decoding a compressed image using a neural image compression network are provided. The method may include generating context parameters associated with a compressed image, the context parameters corresponding to a first area. The method may also include determining that a long range global dependency exists between a first latent and a second latent in a long-range global area within the compressed image and predicting a plurality of context features using a transformer-based long-range context prediction model. The method may then include reconstructing the compressed image based on the predicted plurality of context features.

Abstract: Methods and apparatuses for decoding a compressed image using a neural image compression network may be provided. The method may include generating long-range context model parameters associated with a high resolution compressed image, the long-range context model parameters corresponding to a first area. The method may also include splitting the generated long-range context model parameters into a first number of context parameter blocks. The method may also include for each block in the first number of context parameter blocks, predicting respective context features using a long-range context model and respective context parameter blocks, wherein the long-range context model uses a corner-to-center latent decoding strategy or an edge-to-center latent decoding strategy to decode latents associated with the high resolution compressed image. Then, the high resolution compressed image may be reconstructed based on predicted context features.

Abstract: Embodiments of a semiconductor device and methods of forming thereof are provided herein. In some embodiments, a power semiconductor device may include a first layer having a first conductivity type; a second layer disposed atop the first layer, the second layer having the first conductivity type; a termination region formed in the second layer, the termination region having a second conductivity type opposite the first type; and an active region at least partially formed in the second layer, wherein the active region is disposed adjacent to the termination region proximate a first side of the termination region and wherein the second layer is at least partially disposed adjacent to the termination region proximate a second side of the termination region opposite the first side.

Abstract: Embodiments of a semiconductor device and methods of forming thereof are provided herein. In some embodiments, a power semiconductor device may include a first layer having a first conductivity type; a second layer disposed atop the first layer, the second layer having the first conductivity type; a termination region formed in the second layer, the termination region having a second conductivity type opposite the first type; and an active region at least partially formed in the second layer, wherein the active region is disposed adjacent to the termination region proximate a first side of the termination region and wherein the second layer is at least partially disposed adjacent to the termination region proximate a second side of the termination region opposite the first side.

Abstract: Embodiments of a semiconductor device and methods of forming thereof are provided herein. In some embodiments, a power semiconductor device may include a first layer having a first conductivity type; a second layer disposed atop the first layer, the second layer having the first conductivity type; a termination region formed in the second layer, the termination region having a second conductivity type opposite the first type; and an active region at least partially formed in the second layer, wherein the active region is disposed adjacent to the termination region proximate a first side of the termination region and wherein the second layer is at least partially disposed adjacent to the termination region proximate a second side of the termination region opposite the first side.

Abstract: Embodiments of a semiconductor device and methods of forming thereof are provided herein. In some embodiments, a power semiconductor device may include a first layer having a first conductivity type; a second layer disposed atop the first layer, the second layer having the first conductivity type; a termination region formed in the second layer, the termination region having a second conductivity type opposite the first type; and an active region at least partially formed in the second layer, wherein the active region is disposed adjacent to the termination region proximate a first side of the termination region and wherein the second layer is at least partially disposed adjacent to the termination region proximate a second side of the termination region opposite the first side.

Abstract: Method and system for scheduling professional service procedures using artificial intelligence, specifically, using evolutionary search. Procedures can be scheduled optimally to achieve user defined goals and objectives. The method and system also optimizes future planning of professional service procedures and makes recommendations to the user on how to optimally deploy resources and professionals.