https://scholars.lib.ntu.edu.tw/handle/123456789/607277
標題: | A dense tensor accelerator with data exchange mesh for DNN and vision workloads | 作者: | Lin Y.-S Chen W.-C Yang C.-L SHAO-YI CHIEN CHIA-LIN YANG |
關鍵字: | Neural network hardware;Parallel programming;Vector processors;Building materials;Deep neural networks;Dynamic random access storage;Electronic data interchange;Network architecture;Static random access storage;Tensors;Building blockes;Butterfly networks;Global visibility;Memory efficient;Processing elements;Roofline models;Spatial matching;State of the art;Mesh generation | 公開日期: | 2021 | 卷: | 2021-May | 來源出版物: | Proceedings - IEEE International Symposium on Circuits and Systems | 摘要: | We propose a dense tensor accelerator called VectorMesh, a scalable, memory-efficient architecture that can support a wide variety of DNN and computer vision workloads. Its building block is a tile execution unit (TEU), which includes dozens of processing elements (PEs) and SRAM buffers connected through a butterfly network. A mesh of FIFOs between the TEUs facilitates data exchange between tiles and promote local data to global visibility. Our design performs better according to the roofline model for CNN, GEMM, and spatial matching algorithms compared to state-of-the-art architectures. It can reduce global buffer and DRAM fetches by 2-22 times and up to 5 times, respectively. ? 2021 IEEE |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106629619&doi=10.1109%2fISCAS51556.2021.9401421&partnerID=40&md5=1ee8979c07dbd4736f0054fc07e13b26 https://scholars.lib.ntu.edu.tw/handle/123456789/607277 |
ISSN: | 02714310 | DOI: | 10.1109/ISCAS51556.2021.9401421 |
顯示於: | 電機工程學系 |
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