Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks.
IEEE Transactions on Neural Networks and Learning Systems(2018)
摘要
Convolutional neural networks (CNNs) have led to remarkable progress in a number of key pattern recognition tasks, such as visual scene understanding and speech recognition, that potentially enable numerous applications. Consequently, there is a significant need to deploy trained CNNs to resource-constrained embedded systems. Inference using pretrained modern deep CNNs, however, requires significa...
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关键词
Energy dissipation,Neural networks,Dynamic range,Embedded systems,Training,Quantization (signal),Learning systems
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