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The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures.
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Transfer learning based evolutionary algorithm for composite face sketch recognition
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Min Jiang, Zhongqiang Huang, Liming Qiu, Wenzhen Huang, and Gary G Yen · 2017
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Alistair Shilton, Sunil Gupta, Santu Rana, and Svetha Venkatesh · 2017
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Automated accelerator generation and optimization with composable, parallel and pipeline architecture
Constrained bayesian optimization with noisy experiments
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Timeloop: A systematic approach to dnn accelerator evaluation
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Jason Cong, Peng Wei, Cody Hao Yu, and Peng Zhang · 2018
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Spatial: A language and compiler for application accelerators
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Semi-supervised learning assisted particle swarm optimization of computationally expensive problems
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A survey on evolutionary machine learning
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Population-based black-box optimization for biological sequence design
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Introducing the Next Generation of On-Device Vision Models: MobileNetV3 and MobileNetEdgeTPU
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Bayesian multi-objective hyperparameter optimization for accurate, fast, and efficient neural network accelerator design
Maryam Parsa, John P Mitchell, Catherine D Schuman, Robert M Patton, Thomas E Potok, and Kaushik Roy · 2020
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Prior-guided bayesian optimization
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Automatically designing cnn architectures using the genetic algorithm for image classification
Yanan Sun, Bing Xue, Mengjie Zhang, Gary G Yen, and Jiancheng Lv · 2020
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Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
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