Energy-efficient ConvNets through approximate computing
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Marian Verhelst | Luc Van Gool | Bert Moons | Bert De Brabandere | L. Gool | M. Verhelst | Bert Moons
[1] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[2] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[3] Kaushik Roy,et al. AxNN: Energy-efficient neuromorphic systems using approximate computing , 2014, 2014 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED).
[4] Günhan Dündar,et al. The effects of quantization on multilayer neural networks , 1995, IEEE Trans. Neural Networks.
[5] Luca Benini,et al. A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[6] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[7] Puneet Gupta,et al. Trading Accuracy for Power with an Underdesigned Multiplier Architecture , 2011, 2011 24th Internatioal Conference on VLSI Design.
[8] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[9] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[10] Wei Han,et al. A Flexible Low Power DSP With a Programmable Truncated Multiplier , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.
[11] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[12] Henk Corporaal,et al. Memory-centric accelerator design for Convolutional Neural Networks , 2013, 2013 IEEE 31st International Conference on Computer Design (ICCD).
[13] Georges G. E. Gielen,et al. The Effects of Quantization on Multi-Layer Feedforward Neural Networks , 2003, Int. J. Pattern Recognit. Artif. Intell..
[14] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Kaushik Roy,et al. Quality programmable vector processors for approximate computing , 2013, 2013 46th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[16] Marian Verhelst,et al. DVAS: Dynamic Voltage Accuracy Scaling for increased energy-efficiency in approximate computing , 2015, 2015 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED).
[17] Ninghui Sun,et al. DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning , 2014, ASPLOS.
[18] Kaushik Roy,et al. SALSA: Systematic logic synthesis of approximate circuits , 2012, DAC Design Automation Conference 2012.
[19] Berin Martini,et al. NeuFlow: A runtime reconfigurable dataflow processor for vision , 2011, CVPR 2011 WORKSHOPS.
[20] Lawrence D. Jackel,et al. Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.
[21] Shengen Yan,et al. Deep Image: Scaling up Image Recognition , 2015, ArXiv.