Samsung Electronics Co., Ltd.

Saptarsi Das

Sehwan Lee

Ankur Deshwal

Kiran Kolar Chandrasekharan

Junwoo Jang

Arnab Roy

Dinesh Kumar Yadav

Sabitha Kusuma

Yeongon Cho

Kiran Kolar Chandrasek Haran

Disclosed are a method of accessing a memory and an electronic device for performing the method. The electronic device includes a processor, and a memory electrically connected to the processor, wherein the processor may be configured to select a rank including bank groups of the memory, select a bank corresponding to a memory address to be accessed from among banks included in the selected rank, select a row and one or more columns from rows and columns of the selected bank corresponding to the memory address, and generate the memory address to access the memory based on an address mapping scheme according to the selected rank, the selected bank, the selected row, and the selected one or more columns.

Method and device of accessing memory with near memory accelerator

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.

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Bengaluru, India

Saptarsi Das