Neuromorphic processor-oriented hybrid Q-format multiplication with adaptive quantization for tiny YOLO3

Deep neural networks (DNNs) have delivered unprecedented achievements in the modern Internet of Everything society, encompassing autonomous driving, expert diagnosis, unmanned supermarkets, etc. It continues to be challenging for researchers and engineers to develop a high-performance neuromorphic p...

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Vydáno v:	Neural computing & applications Ročník 35; číslo 15; s. 11013 - 11041
Hlavní autoři:	Li, Tao, Ma, Yitao, Endoh, Tetsuo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.05.2023 Springer Nature B.V
Témata:	Accuracy Algorithms Artificial Intelligence Artificial neural networks Brain research Communication Computational Biology/Bioinformatics Computational Science and Engineering Computer architecture Computer Science Computing time Convolution Data Mining and Knowledge Discovery Energy consumption Feature maps Fixed point arithmetic Format Hardware Image Processing and Computer Vision Measurement Memory Microprocessors Model accuracy Modules Multiplication Multiplication & division Network latency Neural networks Neurons Neurosciences Object recognition Original Article Power consumption Probability and Statistics in Computer Science Processors Representations Semiconductors Transistors Adaptive quantization Neuromorphic processor Fixed-point representation Hybrid multiplication DNNs
ISSN:	0941-0643, 1433-3058
On-line přístup:	Získat plný text
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