Yonsei University

Samsung Electronics Co., Ltd.

Youngsok Kim

Hanjun Kim

Jounghoo Lee

Jinho Lee

Sungjun Cho

Jaeyong Song

Yeongsik Lee

Suhyun Lee

Chaemin Lim

Yeonan Ha

Mingi Yoo

Seonyeong Heo

A processing-in-memory (PIM)-based computing device includes a main memory configured to store first and second data sets, a PIM module configured as an in-line memory module including a memory processor, a working memory, and a memory bank, the memory processor being connected to the working memory and performing a join operation on the first and second data sets loaded into the working memory, and a processor configured to form first and second channels with the main memory and the PIM module, import the first and second data sets through the first channel, and store the first and second data sets in the memory bank through the second channel.

PIM-based computing device for accelerating join operation

An apparatus for accelerating neural networks, includes: a memory for storing graph input data including vertices and edges; an aggregation engine that processes the accumulation of features and generates feature vectors by taking the graph input data and performing an aggregation operation on the graph input data; an on-chip cache for caching the feature vectors; and a combination engine that generates a systolic array for matrix multiplications based on the feature vectors taken from the on-chip cache and weights taken from the memory.

Apparatus for accelerating neural networks

There is provided a graphics processor performance analysis apparatus. The apparatus includes a model definition unit that defines a graphics processor core model for identifying structural stalls of computing and a memory, a data stall of the memory, and an idle stall, a simulation execution unit that simulates an operation of a specific GPU by using architecture parameters of a GPU application and the specific GPU as inputs on the basis of the graphics processor core model, and a performance analysis unit that receives an output of the graphics processor core model as a result of the simulation and analyzes performance of the specific GPU.

Apparatus and method for analyzing graphics processor performance based on graphics processor analytical model

A data processing method and apparatus using a neural network, and an electronic device including the data processing apparatus. The data processing method includes identifying an operator that selects one of a plurality of execution paths for a portion of the neural network while sequentially executing layers included in the neural network, selecting a specific execution path, from among the plurality of execution paths, based on a remaining time that is left for an inference of the neural network, and obtaining a result of the inference of the neural network through the specific execution path.

Data processing method and apparatus using neural network and electronic device including the same

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Seoul, South Korea

Youngsok Kim