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The performance of mobile AI accelerators has been evolving rapidly in the past two years, nearly doubling with each new generation of SoCs.
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Sequence to sequence learning with neural networks
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A convolutional neural network cascade for face detection
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Twitter sentiment analysis with deep convolutional neural networks
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Object tracking benchmark
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A deep reinforcement learning chatbot
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Real-time human activity recognition from accelerometer data using convolutional neural networks
Andrey Ignatov · 2018
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Wespe: weakly supervised photo enhancer for digital cameras
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Ai benchmark: Running deep neural networks on android smartphones
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Pirm challenge on perceptual image enhancement on smartphones: Report
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
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Quantizing deep convolutional networks for efficient inference: A whitepaper
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Relaxed quantization for discretized neural networks
Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, and Max Welling · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
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Ntire 2018 challenge on single image super-resolution: Methods and results
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Synthetic depth-of-field with a single-camera mobile phone
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https://android-developers.googleblog.com/2019/03/introducing-android-q-beta.html
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Data-free quantization through weight equalization and bias correction
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7.1 an 11.5 tops/w 1024-mac butterfly structure dual-core sparsity-aware neural processing unit in 8nm flagship mobile soc
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