Samsung Electronics Co., Ltd.

Ankur Deshwal

Sehwan Lee

Saptarsi Das

Soma Kohli

Junwoo Jang

Keshavan Varadarajan

Kiran Kolar Chandrasekharan

Dinesh Kumar Yadav

Sabitha Kusuma

Sundeep Krishnadasan

Vikash Kumar

Chethan Konanakere Puttanna

Arnab Roy

Kiran Kolar Chandrasek Haran

Yeshwanth Venkatesha

Somya Singhal

Parikshit Kolipaka

Provided is a method and system with deep learning model generation. The method includes identifying a plurality of connections in a neural network that is pre-associated with a deep learning model, generating a plurality of pruned neural networks by pruning different sets of one or more of the plurality of connections to respectively generate each of the plurality of pruned neural networks, generating a plurality of intermediate deep learning models by generating a respective intermediate deep learning model corresponding to each of the plurality of pruned neural networks, and selecting one of the plurality of intermediate deep learning models, having a determined greatest accuracy among the plurality of intermediate deep learning models, to be an optimized deep learning model.

Method and system with deep learning model generation

A method for computing an inner product on a binary data, a ternary data, a non-binary data, and a non-ternary data using an electronic device. The method includes calculating the inner product on a ternary data, designing a fused bitwise data path to support the inner product calculation on the binary data and the ternary data, designing a FPL data path to calculate an inner product between one of the non-binary data and the non-ternary data and one of the binary data and the ternary data, and distributing the inner product calculation for the binary data and the ternary data and the inner product between one of the non-binary data and the non-ternary data and one of the binary data and the ternary data in the fused bitwise data path and the FPL data path.

Electronic device and method for inference binary and ternary neural networks

A method and an apparatus for processing layers in a neural network fetch Input Feature Map (IFM) tiles of an IFM tensor and kernel tiles of a kernel tensor, perform a convolutional operation on the IFM tiles and the kernel tiles by exploiting IFM sparsity and kernel sparsity, and generate a plurality of OFM tiles corresponding to the IFM tiles.

Method and apparatus for processing computation of zero value in processing of layers in neural network

A method of performing convolution in a neural network with variable dilation rate is provided. The method includes receiving a size of a first kernel and a dilation rate, determining at least one of size of one or more disintegrated kernels based on the size of the first kernel, a baseline architecture of a memory and the dilation rate, determining an address of one or more blocks of an input image based on the dilation rate, and one or more parameters associated with a size of the input image and the memory. Thereafter, the one or more blocks of the input image and the one or more disintegrated kernels are fetched from the memory, and an output image is obtained based on convolution of each of the one or more disintegrated kernels and the one or more blocks of the input image.

Method and system of performing convolution in neural networks with variable dilation rate

A method and apparatus to construct a bounding volume hierarchy (BVH) tree includes: generating 2-dimensional (2D) tiles including primitives; converting the 2D tiles into 3-dimensional (3D) tiles; and constructing the BVH tree based on the 3D tiles.

System and method of constructing bounding volume hierarchy tree

Provide are a methods and devices for processing graphics data in a graphics processing unit (GPU). The method of processing graphics data includes receiving, at a processor, a difference of Gaussian (DOG) layer of an image, detecting, from the received DOG layer, a candidate DOG layer of the image as an intermediate layer, detecting at least one extreme point by comparing values of the candidate DOG layer with values of a previous DOG layer and a next DOG layer, and storing the at least one extreme point in a buffer.

Method and device for processing graphics data in graphics processing unit

A system and a method for information acquisition of Wireless Sensor Network (WSN) data as a cloud based service are provided. An apparatus in the system including a WSN, a service cloud, and a device, includes a virtual sensor configured to receive data from a physical sensor in the WSN. The apparatus further includes a virtual sensor controller configured to receive a request for the data from the service cloud or the device, and spawn a virtual machine (VM) based on the request. The apparatus further includes the VM configured to transmit the data to the service cloud or the device.

Growing community of inventors

Uttar Pradesh, India

Ankur Deshwal