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CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices.

, , , , , , , , , , , , , , , and . MICRO, page 395-408. ACM, (2017)

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Klotski: DNN Model Orchestration Framework for Dataflow Architecture Accelerators., , , , , , and . ICCAD, page 1-9. IEEE, (2023)Wonderland: A Novel Abstraction-Based Out-Of-Core Graph Processing System., , , , , and . ASPLOS, page 608-621. ACM, (2018)HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array., , , , , and . HPCA, page 56-68. IEEE, (2019)GraphQ: Scalable PIM-Based Graph Processing., , , , , , and . MICRO, page 712-725. ACM, (2019)E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs., , , , , , , , , and 1 other author(s). HPCA, page 69-80. IEEE, (2019)Prague: High-Performance Heterogeneity-Aware Asynchronous Decentralized Training., , , and . ASPLOS, page 401-416. ACM, (2020)ASPLOS 2020 was canceled because of COVID-19..AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators., , , , , and . HPCA, page 342-355. IEEE, (2020)CSE: Parallel Finite State Machines with Convergence Set Enumeration., , , , , , and . MICRO, page 29-41. IEEE Computer Society, (2018)Distributed Graph Processing System and Processing-in-memory Architecture with Precise Loop-carried Dependency Guarantee., , , , , , , and . ACM Trans. Comput. Syst., 37 (1-4): 5:1-5:37 (2019)Heterogeneity-Aware Asynchronous Decentralized Training., , , and . CoRR, (2019)