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A timed HW/SW coemulation technique for fast yet accurate system verification., , и . ICSAMOS, стр. 74-81. IEEE, (2009)An efficient parallelization technique for x264 encoder on heterogeneous platforms consisting of CPUs and GPUs., , и . J. Real-Time Image Processing, 9 (1): 5-18 (2014)NNsim: fast performance estimation based on sampled simulation of GPGPU kernels for neural networks., , , и . DAC, стр. 176:1-176:6. ACM, (2018)Hardware-in-the-loop Simulation for CPU/GPU Heterogeneous Platforms., , , , и . DAC, стр. 138:1-138:6. ACM, (2014)Active disk meets flash: a case for intelligent SSDs., , , , , и . ICS, стр. 91-102. ACM, (2013)GOPipe: a granularity-oblivious programming framework for pipelined stencil executions on GPU., , , , и . PPoPP, стр. 431-432. ACM, (2019)Trace-driven HW/SW cosimulation using virtual synchronization technique., , и . DAC, стр. 345-348. ACM, (2005)GOPipe: A Granularity-Oblivious Programming Framework for Pipelined Stencil Executions on GPU., , , , и . PACT, стр. 43-54. ACM, (2020)BPNet: Branch-pruned conditional neural network for systematic time-accuracy tradeoff in DNN inference: work-in-progress., и . CODES+ISSS, стр. 2:1-2:2. ACM, (2019)BPNet: Branch-pruned Conditional Neural Network for Systematic Time-accuracy Tradeoff., , и . DAC, стр. 1-6. IEEE, (2020)