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Symphony: Orchestrating Sparse and Dense Tensors with Hierarchical Heterogeneous Processing.

, , , , , , , , , , , и . ACM Trans. Comput. Syst., (2023)

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Computer Architectures for Mobile Computer Vision Systems.. University of Michigan, USA, (2013)GA3C: GPU-based A3C for Deep Reinforcement Learning., , , , и . CoRR, (2016)MVSS: Michigan Visual Sonification System., , , , и . ESPA, стр. 143-146. IEEE, (2012)vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design., , , , и . MICRO, стр. 18:1-18:13. IEEE Computer Society, (2016)Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture., , , , , , , , , и 7 other автор(ы). MICRO, стр. 14-27. ACM, (2019)Augmenting Legacy Networks for Flexible Inference., , , , и . IV, стр. 1-8. IEEE, (2023)A 0.32-128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm., , , , , , , , , и 7 other автор(ы). IEEE J. Solid State Circuits, 55 (4): 920-932 (2020)Buffets: An Efficient and Composable Storage Idiom for Explicit Decoupled Data Orchestration., , , , , , , , и . ASPLOS, стр. 137-151. ACM, (2019)Augmenting Legacy Networks for Flexible Inference., , , , и . ECCV Workshops (7), том 13807 из Lecture Notes in Computer Science, стр. 84-98. Springer, (2022)A real-time energy-efficient superpixel hardware accelerator for mobile computer vision applications., , , , , и . DAC, стр. 95:1-95:6. ACM, (2016)