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Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks.
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Earlier work this paper cites.
cudnn: Efficient primitives for deep learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally · 2015
Earlier work this paper cites.
Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip
Filipp Akopyan, Jun Sawada, Andrew Cassidy, Rodrigo Alvarez-Icaza, John Arthur, Paul Merolla, Nabil Imam, Yutaka Nakamura, Pallab Datta, Gi-Joon Nam, et al · 2015
Earlier work this paper cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
Earlier work this paper cites.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
Earlier work this paper cites.
Fixed point quantization of deep convolutional networks
Darryl Lin, Sachin Talathi, and Sreekanth Annapureddy · 2016
Earlier work this paper cites.
Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
Matthieu Courbariaux and Yoshua Bengio · 2016
Earlier work this paper cites.
Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks
Yu-Hsin Chen, Tushar Krishna, Joel S Emer, and Vivienne Sze · 2016
Cited alongside, same era.
Fast algorithms for convolutional neural networks
Andrew Lavin and Scott Gray · 2016
Cited alongside, same era.
Isaac: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars
Ali Shafiee, Anirban Nag, Naveen Muralimanohar, Rajeev Balasubramonian, John Paul Strachan, Miao Hu, R Stanley Williams, and Vivek Srikumar · 2016
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten · 2017
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit
Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al · 2017
Later among the works it cites.
Hello edge: Keyword spotting on microcontrollers
Yundong Zhang, Naveen Suda, Liangzhen Lai, and Vikas Chandra · 2017
Later among the works it cites.
Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural networks
Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Joon Kyung Kim, Vikas Chandra, and Hadi Esmaeilzadeh · 2017
Later among the works it cites.
https://petewarden.com/2018/06/11/why-the-future-of-machine-learning-is-tiny/
2018
Closest in time.
Quantizing convolutional neural networks for low-power high-throughput inference engines
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Accelerating persistent neural networks at datacenter scale
Eric Chung, Jeremy Fowers, Kalin Ovtcharov, Michael Papamichael, Adrian Caulfield, Todd Massengil, Ming Liu, Daniel Lo, Shlomi Alkalay, Michael Haselman, et al · 2017
Cited alongside, same era.
Deep convolutional neural network inference with floating-point weights and fixed-point activations
Liangzhen Lai, Naveen Suda, and Vikas Chandra · 2017
Cited alongside, same era.
https://community.arm.com/processors/b/blog/posts/machine-learning-moving-to-the-network-edge-to-improve-next-generation-services
Cited in the paper.
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite
Cited in the paper.
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla
Cited in the paper.
https://github.com/onnx
Cited in the paper.
https://developer.android.com/ndk/guides/neuralnetworks/
Cited in the paper.
Sean O Settle, Manasa Bollavaram, Paolo D’Alberto, Elliott Delaye, Oscar Fernandez, Nicholas Fraser, Aaron Ng, Ashish Sirasao, and Michael Wu · 2018
Closest in time.
Cmsis-nn: Efficient neural network kernels for arm cortex-m cpus
Liangzhen Lai, Naveen Suda, and Vikas Chandra · 2018
Closest in time.
Not all ops are created equal!
Liangzhen Lai, Naveen Suda, and Vikas Chandra · 2018
Closest in time.