Resource efficient activation functions for neural network accelerators
Tài liệu tham khảo
Amin, 1997, Piecewise linear approximation applied to nonlinear function of a neural network, IEE Proc.-Circuits Devices Syst., 144, 313, 10.1049/ip-cds:19971587
D. Aymeric, Tensorflow examples (2017). URL: https://bit.ly/327SNUN (accessed 3 June 2019).
Azari, 2019, An energy-efficient reconfigurable lstm accelerator for natural language processing, 4450
Basterretxea, 2007, An experimental study on nonlinear function computation for neural/fuzzy hardware design, IEEE Trans. Neural Networks, 18, 266, 10.1109/TNN.2006.884680
J. Brownlee, Sequence classification with LSTM recurrent neural networks in python with keras (2019). URL: https://bit.ly/3iNfhB5 (accessed 3 March 2019).
Cao, 2019, Efficient and effective sparse lstm on FPGA with bank-balanced sparsity, 63
A.X.M. Chang, B. Martini, E. Culurciello, Recurrent neural networks hardware implementation on FPGA (2015). arXiv preprint arXiv:1511.05552.
Chen, 2014, Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning, ACM SIGARCH Computer Architecture News, 42, 269, 10.1145/2654822.2541967
D.-A. Clevert, T. Unterthiner, S. Hochreiter, Fast and accurate deep network learning by exponential linear units (ELUs), ICLR, 2015.
Courbariaux, 2014, Training deep neural networks with low precision multiplications, International Conference on Learning Representation
Deng, 2018, GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework, Neural Networks, 100, 49, 10.1016/j.neunet.2018.01.010
Desjardins, 2015, Natural neural networks, Advances in Neural Information Processing Systems, 2071
Elfwing, 2018, Sigmoid-weighted linear units for neural network function approximation in reinforcement learning, Neural Networks, 107, 3, 10.1016/j.neunet.2017.12.012
Fan, 2013, Efficient VLSI architecture for training radial basis function networks, Sensors, 13, 3848, 10.3390/s130303848
Fayek, 2017, Matdl: A lightweight deep learning library in matlab, J. Open Source Software, 2, 413, 10.21105/joss.00413
Fernando, 2018, Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection, Neural Networks, 108, 466, 10.1016/j.neunet.2018.09.002
Ferreira, 2016, An FPGA implementation of a long short-term memory neural network, 1
Gomar, 2016, Precise digital implementations of hyperbolic tanh and sigmoid function, 1586
Guan, 2017, FPGA-based accelerator for long short-term memory recurrent neural networks, 629
Hajduk, 2017, High accuracy FPGA activation function implementation for neural networks, Neurocomputing, 247, 59, 10.1016/j.neucom.2017.03.044
Han, 2017, Ese: Efficient speech recognition engine with sparse lstm on FPGA, 75
K. Harrison, RNN w/ LSTM cell example in tensorflow and python (2016). URL: https://bit.ly/31Z4fC1 (accessed 3 June 2019).
He, 2015, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, 1026
He, 2016, Deep residual learning for image recognition, 770
D. Hendrycks, K. Gimpel, Gaussian error linear units (gelus) (2016). arXiv preprint arXiv:1606.08415.
Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735
K. Hornik, M. Stinchcombe, H. White, et al., Multilayer feedforward networks are universal approximators, Neural Networks 2(5) (1989) 359–366.
Hubara, 2016, Binarized neural networks, Advances in Neural Information Processing Systems, 4107
Intel, Cyclone V device overview (2018). URL: https://intel.ly/2UbUwVY.
Kaggle. US baby names: Explore naming trends from babies born in the US (2016).
Karevan, 2020, Transductive LSTM for time-series prediction: An application to weather forecasting, Neural Networks, 125, 1, 10.1016/j.neunet.2019.12.030
Klambauer, 2017, Self-normalizing neural networks, Advances in Neural Information Processing Systems, 971
Kouretas, 2018, Hardware aspects of long short term memory, 525
Larkin, 2006, An efficient hardware architecture for a neural network activation function generator, 1319
Lee, 2016, FPGA-based low-power speech recognition with recurrent neural networks, 230
M. Leshno, V.Y. Lin, A. Pinkus, S. Schocken, Multilayer feedforward networks with a nonpolynomial activation function can approximate any function, Neural Networks 6(6) (1993) 861–867.
Liao, 2020, An intelligent low-power low-cost mobile lab-on-chip yeast cell culture platform, IEEE Access, 8, 70733, 10.1109/ACCESS.2020.2987206
M. Lin, Q. Chen, S. Yan, Network in network (2013). arXiv preprint arXiv:1312.4400.
Maas, 2013, Rectifier nonlinearities improve neural network acoustic models, Proc. ICML, 30, 1
M. Marcus, B. Santorini, M.A. Marcinkiewicz, Building a large annotated corpus of english: The penn treebank (1993).
Merity, 2017, Pointer sentinel mixture models, International Conference on Learning Representation
Moss, 2018, A two-speed, radix-4, serial–parallel multiplier, IEEE Trans. Very Large Scale Integr. VLSI Syst., 27, 769, 10.1109/TVLSI.2018.2883645
Pushpa, 2014, Implementation of hyperbolic tangent activation function in vlsi, Int. J. Adv. Res. Comput. Sci. Technol., 2, 225
Ramachandran, 2017, Searching for activation functions, ICLR Workshop Track
G. Roopal, LSTM tensorflow (2017). URL: https://bit.ly/3hhNc4n (accessed 3 June 2019).
Rybalkin, 2018, Finn-l: Library extensions and design trade-off analysis for variable precision lstm networks on FPGAs, 89
Saichand, 2008, FPGA realization of activation function for artificial neural networks, vol. 3, 159
Scardapane, 2019, Kafnets: Kernel-based non-parametric activation functions for neural networks, Neural Networks, 110, 19, 10.1016/j.neunet.2018.11.002
Sidek, 2020, Booth algorithm with implementation of UART module using FPGA, Int. J. Integr. Eng., 12, 151
Siegel, 2020, Approximation rates for neural networks with general activation functions, Neural Networks, 10.1016/j.neunet.2020.05.019
Sim, 2017, A new stochastic computing multiplier with application to deep convolutional neural networks, 1
Sim, 2019, Cost-effective stochastic MAC circuits for deep neural networks, Neural Networks, 117, 152, 10.1016/j.neunet.2019.04.017
Simonyan, 2015, Very deep convolutional networks for large-scale image recognition, Internation Conference on Learning
Sze, 2017, Efficient processing of deep neural networks: A tutorial and survey, Proc. IEEE, 105, 2295, 10.1109/JPROC.2017.2761740
Tan, 2007
Terada, 2020, Fast generalization error bound of deep learning without scale invariance of activation functions, Neural Networks, 10.1016/j.neunet.2020.05.033
Tiwari, 2015, Hardware implementation of neural network with sigmoidal activation functions using cordic, Microprocess. Microsyst., 39, 373, 10.1016/j.micpro.2015.05.012
Tommiska, 2003, Efficient digital implementation of the sigmoid function for reprogrammable logic, IEE Proc.-Comput. Digital Tech., 150, 403, 10.1049/ip-cdt:20030965
Tsmots, 2019, Hardware implementation of sigmoid activation functions using fpga, 34
N. Virdee, Lstm neural network from scratch (2018).
Wang, 2019, Deep neural network approximation for custom hardware: Where we’ve been, where we’re going, ACM Computing Surveys (CSUR), 52, 1, 10.1145/3214306
Wang, 2018, C-lstm: Enabling efficient LSTM using structured compression techniques on FPGAs, 11
L. Wei, Convolutional neural networks for cifar-10 (2018). URL: https://github.com/BIGBALLON/cifar-10-cnn (accessed 25 May 2019).
Wu, 2020, Accuracy tolerant neural networks under aggressive power optimization, 774
Wuraola, 2021, Efficient activation functions for embedded inference engines, Neurocomputing, 442, 73, 10.1016/j.neucom.2021.02.030
H. Xiao, K. Rasul, R. Vollgraf, Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms (2017). arXiv preprint arXiv:1708.07747.
Yang, 2018, Design space exploration of neural network activation function circuits, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 38, 1974, 10.1109/TCAD.2018.2871198
W. Zaremba, I. Sutskever, O. Vinyals, Recurrent neural network regularization (2014). arXiv preprint arXiv:1409.2329.
