Constraint-Aware Deep Neural Network Compression

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Similarity-Preserving Knowledge Distillation (2019, background): “For example, unstructured pruning requires optimized sparse matrix multiplication routines to realize practical acceleration [26], platform constraint-aware compression [2, 36, 37] requires hardware simulators or empirical measurements, and arbitrarybit quantization [9, 17] requires specialized hard”
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation (2021, background): “In recent years, there has been increasing interest in better aligning the resource consumption of neural networks with the computational constraints of the platforms on which they will be deployed [7,36,38].”
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks (2019, background): “Thus, others explore pruning entire ﬁlters (Li et al., 2016; Liu et al., 2017), with some even targeting device-centric metrics, such as ﬂoating-point operations (Tang et al., 2018) and latency (Chen et al., 2018).”
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning (2019, methodology): “AutoML Recently, AutoML methods [21, 52, 8, 12] take the real-time inference latency on multiple devices into account to iteratively prune channels in different layers of a network via reinforcement learning [21] or an automatic feedback loop [52].”