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CANDLES: Channel-Aware Novel Dataflow-Microarchitecture Co-Design for Low Energy Sparse Neural Network Acceleration.

, , , , and . HPCA, page 876-891. IEEE, (2022)

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INXS: Bridging the throughput and energy gap for spiking neural networks., , and . IJCNN, page 2451-2459. IEEE, (2017)Impact of Serial Scaling of Multi-threaded Programs in Many-Core Era., , and . SBAC-PAD (Workshops), page 36-41. IEEE Computer Society, (2014)Wire-Aware Architecture and Dataflow for CNN Accelerators., , , , , and . MICRO, page 1-13. ACM, (2019)An empirical high level performance model for future many-cores., , and . Conf. Computing Frontiers, page 1:1-1:8. ACM, (2015)CANDLES: Channel-Aware Novel Dataflow-Microarchitecture Co-Design for Low Energy Sparse Neural Network Acceleration., , , , and . HPCA, page 876-891. IEEE, (2022)A Case for Dynamic Activation Quantization in CNNs., , and . EMC2@ASPLOS, page 4-8. IEEE, (2018)SpinalFlow: An Architecture and Dataflow Tailored for Spiking Neural Networks., , , , and . ISCA, page 349-362. IEEE, (2020)Moving CNN Accelerator Computations Closer to Data., , and . EMC2@ASPLOS, page 34-38. IEEE, (2018)Sequential and Parallel Code Sections are Different: they may require different Processors., and . PARMA-DITAM@HiPEAC, page 13-18. ACM, (2015)Modeling performance of serial and parallel sections of multi-threaded programs in manycore era. (Modélisation de la performance des sections séquentielles et parallèles au sein de programmes multithreadés à l'ère des many-coeurs).. University of Rennes 1, France, (2015)