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Multiplier-less Artificial Neurons exploiting error resiliency for energy-efficient neural computing.

, , , and . DATE, page 145-150. IEEE, (2016)

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Workload-aware Automatic Parallelization for Multi-GPU DNN Training., , , , , and . CoRR, (2018)DyVEDeep: Dynamic Variable Effort Deep Neural Networks., , , and . CoRR, (2017)Exploring Spin-Transfer-Torque Devices for Logic Applications., , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 34 (9): 1441-1454 (2015)Energy-Efficient Reduce-and-Rank Using Input-Adaptive Approximations., , , and . IEEE Trans. Very Large Scale Integr. Syst., 25 (2): 462-475 (2017)Efficient AI System Design With Cross-Layer Approximate Computing., , , , , , , , , and 30 other author(s). Proc. IEEE, 108 (12): 2232-2250 (2020)ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training., , , , , , , , , and 1 other author(s). CoRR, (2021)Efficacy of Pruning in Ultra-Low Precision DNNs., , and . ISLPED, page 1-6. IEEE, (2021)Efficient embedded learning for IoT devices., , and . ASP-DAC, page 308-311. IEEE, (2016)Multiplier-less Artificial Neurons exploiting error resiliency for energy-efficient neural computing., , , and . DATE, page 145-150. IEEE, (2016)Scalable-effort classifiers for energy-efficient machine learning., , , and . DAC, page 67:1-67:6. ACM, (2015)