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Resistive Bloom filters: From approximate membership to approximate computing with bounded errors.

, , and . DATE, page 1441-1444. IEEE, (2016)

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An efficient network on-chip architecture based on isolating local and non-local communications., , , and . DATE, page 350-353. EDA Consortium San Jose, CA, USA / ACM DL, (2013)Energy-efficient neural networks using approximate computation reuse., , , and . DATE, page 1223-1228. IEEE, (2018)Associative Convolutional Layers., , , and . CoRR, (2019)Quiver: Using Control Perturbations to Increase the Observability of Sensor Data in Smart Buildings., , , , and . CoRR, (2016)Associative Convolutional Layers., , , , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 3115-3123. PMLR, (2021)Accelerating Local Binary Pattern Networks with Software-Programmable FPGAs., , , , and . DATE, page 1112-1117. IEEE, (2019)LEMAX: learning-based energy consumption minimization in approximate computing with quality guarantee., , and . DAC, page 161:1-161:6. ACM, (2018)An efficient network on-chip architecture based on isolating local and non-local communications., , , and . Comput. Electr. Eng., (2015)Algorithm-Hardware Optimization of Deep Neural Networks for Edge Applications.. University of California, San Diego, USA, (2020)SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks., , , , and . ISCA, page 662-673. IEEE Computer Society, (2018)