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Enhancing the Programmability and Performance Portability of GPU Tensor Operations., , , , and . Euro-Par, volume 11725 of Lecture Notes in Computer Science, page 213-226. Springer, (2019)Boda: A Holistic Approach for Implementing Neural Network Computations., , and . Conf. Computing Frontiers, page 53-62. ACM, (2017)FireCaffe: near-linear acceleration of deep neural network training on compute clusters., , , and . CoRR, (2015)Shallow Networks for High-accuracy Road Object-detection., , , , and . VEHITS, page 33-40. SciTePress, (2017)Boda-RTC: Productive generation of portable, efficient code for convolutional neural networks on mobile computing platforms., , and . WiMob, page 1-10. IEEE Computer Society, (2016)Fast cycle-accurate simulation and instruction set generation for constraint-based descriptions of programmable architectures., , , , and . CODES+ISSS, page 18-23. ACM, (2004)Matching Architecture to Application Via Configurable Processors: A Case Study with Boolean Satisfiability Problem., , , , and . ICCD, page 447-452. IEEE Computer Society, (2001)Implementing Efficient, Portable Computations for Machine Learning.. University of California, Berkeley, USA, (2017)base-search.net (ftcdlib:qt4nz902vt).libHOG: Energy-Efficient Histogram of Oriented Gradient Computation., , and . ITSC, page 1248-1254. IEEE, (2015)SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size, , , , , and . (2016)cite arxiv:1602.07360Comment: In ICLR Format.