Samsung Electronics Co., Ltd.

Hyung-Dal Kwon

Hanmin Park

Jae-Eon Jo

Jaehyeong Sim

Seung Wook Lee

Seungwook Lee

Dae Hyun Kim

Young Nam Hwang

Gyeong Min Ha

Ho-Suk Na

Ho Young Kim

Byungwoo Bang

Sunghoon Son

An apparatus with deep learning includes: a systolic adder tree including adder trees connected in row and column directions; and an input multiplexer connected to an input register of at least one of the adder trees and configured to determine column directional data movement between the adder trees based on operation modes.

Method and apparatus with deep learning operations with adder tree structure

Disclosed is a method and apparatus with deep learning operations. A deep learning apparatus includes a processor, configured to support a plurality of different operation modes, including a systolic array having a plurality of multiplier accumulator (MAC) units, and a control circuit configured to respectively control, for each the plurality of different operation modes, select operations of the plurality of MAC units and data movements among the plurality of MAC units.

Method and apparatus with deep learning operations

A computing device and method is disclosed. The computing device includes a plurality of processing cores, and a tile scheduler configured to update a cost matrix of each of the plurality of processing cores based on meta information of each of first tiles previously allocated to the plurality of processing cores and meta information of each of second tiles, and allocate the second tiles with respect to the plurality of processing cores using the updated cost matrix of each of the plurality of processing cores.

Computing device and method for allocating resources using cost matrix

An accelerator, a method of operating the accelerator, and an electronic device including the accelerator. A method of operating the accelerator configured to perform a target operation includes packing input data with a data layout determined based on a word width of a memory in the accelerator and a spatial size of a filter to be applied to the target operation and storing the packed input data in the memory, and performing the target operation between a portion of the input data stored in a same word in the memory and weights of the filter.

Accelerator, method of operating an accelerator, and electronic device including an accelerator

Provided is a neural processing unit that performs application-work including a first neural network operation, the neural processing unit includes a first processing core configured to execute the first neural network operation, a hardware block reconfigurable as a hardware core configured to perform hardware block-work, and at least one processor configured to execute computer-readable instructions to distribute a part of the application-work as the hardware block-work to the hardware block based on a first workload of the first processing core.

Neural processing unit, neural processing system, and application system

Disclosed is an apparatus and method for performing deep learning operations. The apparatus includes a systolic array comprising multiplier accumulator (MAC) units, and a control circuit configured to control an operation of a multiplexer connected to at least one of the MAC units and operations of the MAC units according to a plurality of operation modes.

Method and apparatus for performing deep learning operations

A memory device includes a memory configured to store input data and filter data for a convolution operation, and a function processor configured to, in response to a read command of at least a portion of data from among the input data and the filter data, transform the at least a portion of the data based on a parameter of the convolution operation during a clock cycle corresponding to the read command and output a corresponding transformation result as transformed data.

Memory device and method

An operating method of an electronic device including controllers includes updating, by a first-level controller of the controllers, a first-level firmware of the the first-level controller, writing, by the first-level controller, a second-level firmware to one of second-level controllers of the controllers having a lower level than the first-level controller, booting, by the one of the second-level controllers, by performing a reset operation, verifying, by the first-level controller or the booted second-level controller, whether there is a target second-level controller with out-of-date firmware, and writing, by the first-level controller or the booted second-level controller in response to a result of the verifying, the second-level firmware to the target second-level controller.

Management method and device for updating underlying multi-level firmware programs using verifying controllers

A neural network deep learning data control apparatus includes: a memory; an encoding circuit configured to receive a data sequence, generate a compressed data sequence in which consecutive invalid bits in a bit string of the data sequence are compressed into a single bit of the compressed data sequence, generate a validity determination sequence indicating a valid bit and an invalid bit in a bit string of the compressed data sequence, and write the compressed data sequence and the validity determination sequence to the memory; and a decoding circuit configured to read the compressed data sequence and the validity determination sequence from the memory, and determine a bit in the bit string of the compressed data sequence set for transmission to a neural network circuit, based on the validity determination sequence, such that the neural network circuit omits an operation with respect to non-consecutive invalid bits.

Method and apparatus with neural network data input and output control

An operating method of a power optimization scheduler is provided, where the operating method of a power optimization scheduler includes obtaining information regarding a neural network (NN) model, determining a voltage value for a task to be performed by at least one processing device, based on the obtained information regarding the NN model, and controlling a power management device to apply a voltage corresponding to the determined voltage value to the at least one processing device.

Operating method of power optimization scheduler and computing apparatus including power optimization scheduler

A touch screen controller (TSC) includes: a front end circuit configured to send a control signal to a touch panel and to receive a touch signal from the touch panel; an algorithm processing circuit configured to process source data generated based on the touch signal according to a predetermined algorithm; a memory configured to store the source data and result data obtained as a result of processing the source data at the algorithm processing circuit; and a bus configured to transfer data among the front end circuit, the algorithm processing circuit, and the memory. The algorithm processing circuit includes: a buffer configured to temporarily store the source data or the result data and shared by at least two circuits; and a special function register (SFR) configured to store a setting value necessary for an operation of the algorithm processing circuit.

Growing community of inventors

Hwaseong-si, South Korea

Hyung-Dal Kwon