TY - JOUR
T1 - A Systematic Study of Tiny YOLO3 Inference
T2 - Toward Compact Brainware Processor with Less Memory and Logic Gate
AU - Li, Tao
AU - Ma, Yitao
AU - Endoh, Tetsuo
N1 - Funding Information:
This work was partially supported by Crossministerial Strategic Innovation Promotion Program (SIP) 2nd Phase-Physical Space Digital Processing Platform: ‘‘R&D of Ultra-Low Power IoT Devices and Its Technical Platform with MTJ/CMOS Hybrid Technologies for Society 5.0’’, Cabinet Office, and Japan Science and Technology Agency-Open Innovation Platform with Enterprises, Research Institute and Academia (JSTOPERA), grant number JPMJOP1611, and Center for Innovative Integrated Electronic Systems (CIES) consortium.
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - The emerging of deep neural networks, especially the convolutional neural network (CNN), substantially promotes the fast development of brainware processors in object detection. However, the vast network architecture brings severe challenges to the design of brainware processor, which requires a large number of logic gates and memories. Therefore, a compact brainware processor with less memory and logic gate has a high demand in object detection. Typically, the object detection involves single-shot and multi-shot detectors in accordance with different detection principle. In the early stage, the multi-shot detector has a leading role in solving object detection issues, such as region-based convolutional neural networks (R-CNNs), faster R-CNNs etc. However, the multi-shot detector suffers from a low detection rate comparing with the single-shot detector. The you only look once (YOLO) algorithm, as the state-of-the-art real-time object detection algorithm, receives extensive attention from the academics and industry. Particularly, the lightweight YOLO algorithm, tiny YOLO3, has excellent potential for circuit design of compact brainware processor. Nonetheless, systematic studies of tiny YOLO3 are still missing up to the present. This paper offers a thorough review of the tiny YOLO3 algorithm, which can fill the gap in the field of object detection. Furthermore, the open solutions of compressing the tiny YOLO3 algorithm are proposed from the aspects of algorithm, hardware and emerging technology. The comprehensive study presented in this paper can not only enhance understanding of the tiny YOLO3 algorithm for researchers or engineers but also make a significant contribution to accelerating the development of compact brainware processor.
AB - The emerging of deep neural networks, especially the convolutional neural network (CNN), substantially promotes the fast development of brainware processors in object detection. However, the vast network architecture brings severe challenges to the design of brainware processor, which requires a large number of logic gates and memories. Therefore, a compact brainware processor with less memory and logic gate has a high demand in object detection. Typically, the object detection involves single-shot and multi-shot detectors in accordance with different detection principle. In the early stage, the multi-shot detector has a leading role in solving object detection issues, such as region-based convolutional neural networks (R-CNNs), faster R-CNNs etc. However, the multi-shot detector suffers from a low detection rate comparing with the single-shot detector. The you only look once (YOLO) algorithm, as the state-of-the-art real-time object detection algorithm, receives extensive attention from the academics and industry. Particularly, the lightweight YOLO algorithm, tiny YOLO3, has excellent potential for circuit design of compact brainware processor. Nonetheless, systematic studies of tiny YOLO3 are still missing up to the present. This paper offers a thorough review of the tiny YOLO3 algorithm, which can fill the gap in the field of object detection. Furthermore, the open solutions of compressing the tiny YOLO3 algorithm are proposed from the aspects of algorithm, hardware and emerging technology. The comprehensive study presented in this paper can not only enhance understanding of the tiny YOLO3 algorithm for researchers or engineers but also make a significant contribution to accelerating the development of compact brainware processor.
KW - CNN
KW - Tiny YOLO3
KW - brainware processor
KW - deep neural network
KW - hardware acceleration
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U2 - 10.1109/ACCESS.2020.3013934
DO - 10.1109/ACCESS.2020.3013934
M3 - Article
AN - SCOPUS:85089949617
VL - 8
SP - 142931
EP - 142955
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9154668
ER -