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Design Considerations for High Performance RF Cores Based on Process Variation Study.

, , , , , , and . J. Electron. Test., 24 (1-3): 143-155 (2008)

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Invited - Cross-layer approximations for neuromorphic computing: from devices to circuits and systems., , , , , , and . DAC, page 98:1-98:6. ACM, (2016)Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks., , and . CoRR, (2017)Deep Spiking Convolutional Neural Network Trained With Unsupervised Spike-Timing-Dependent Plasticity., , , and . IEEE Trans. Cognitive and Developmental Systems, 11 (3): 384-394 (2019)Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation., , , and . CoRR, (2020)Training Deep Spiking Neural Networks for Energy-Efficient Neuromorphic Computing., , , , , and . ICASSP, page 8549-8553. IEEE, (2020)Enabling Homeostasis using Temporal Decay Mechanisms in Spiking CNNs Trained with Unsupervised Spike Timing Dependent Plasticity., , , and . IJCNN, page 1-8. IEEE, (2020)sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network With On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge., , , and . IEEE Trans. Circuits Syst. I Regul. Pap., 67-I (8): 2546-2555 (2020)Reinforcement Learning with Low-Complexity Liquid State Machines., , and . CoRR, (2019)Test Coverage Analysis for Designs with Timing Exceptions., and . ATS, page 169-174. IEEE Computer Society, (2017)EnsembleSNN: Distributed assistive STDP learning for energy-efficient recognition in spiking neural networks., , and . IJCNN, page 2629-2635. IEEE, (2017)