Spatial Data Dependence Graph Simulator for Convolutional Neural Network Accelerators

被引：0
|
作者：
Wang, Jooho [1 ]
Kim, Jiwon [1 ]
Moon, Sungmin [1 ]
Kim, Sunwoo [1 ]
Park, Sungkyung [2 ]
Park, Chester Sungchung [1 ]
机构：
[1] Konkuk Univ, Dept Elect Engn, Seoul, South Korea
[2] Pusan Natl Univ, Dept Elect Engn, Busan, South Korea
来源：
2019 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2019) | 2019年
关键词：
convolution neural network (CNN); accelerator; dataflow; data dependence graph;
D O I：
10.1109/aicas.2019.8771561
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A spatial data dependence graph (S-DDG) is newly proposed to model an accelerator dataflow. The pre-RTL simulator based on the S-DDG helps to explore the design space in the early design phase. The simulation results show the impact of memory latency and bandwidth on a convolutional neural network (CNN) accelerator.
引用
收藏
页码：309 / 310
页数：2
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