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Maximum Power Estimation Using the Limiting Distributions of Extreme Order Statistics.

, , and . DAC, page 684-689. ACM Press, (1998)

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A General Framework to Map Neural Networks onto Neuromorphic Processor., , , , and . ISQED, page 20-25. IEEE, (2019)Energy-efficient, high-performance, highly-compressed deep neural network design using block-circulant matrices., , , , , and . ICCAD, page 458-465. IEEE, (2017)Improving energy efficiency for energy harvesting embedded systems., , and . ASP-DAC, page 497-502. IEEE, (2013)An Event-driven Neuromorphic System with Biologically Plausible Temporal Dynamics., , , , and . ICCAD, page 1-8. ACM, (2019)Efficient Recurrent Neural Networks using Structured Matrices in FPGAs., , , , , and . ICLR (Workshop), OpenReview.net, (2018)The stochastic modeling of TiO2 memristor and its usage in neuromorphic system design., , , , and . ASP-DAC, page 831-836. IEEE, (2014)Task allocation for minimum system power in a homogenous multi-core processor., and . Green Computing Conference, page 299-306. IEEE Computer Society, (2010)Stable spike-timing dependent plasticity rule for multilayer unsupervised and supervised learning., , , and . IJCNN, page 1999-2006. IEEE, (2017)Profile-Based Low Power Scheduling for Conditional Task Graph: A Communication Aware Approach., , and . ISCAS, page 2116-2119. IEEE, (2007)MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level., , , and . ICPR, page 8275-8282. IEEE, (2020)