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Showing results 1 to 13 of 13

Issue DateTitleAuthor(s)TypeView
2017-06-18A New Stochastic Computing Multiplier with Application to Deep Convolutional Neural NetworksSim, Hyeonuk; Lee, JongeunCONFERENCE65
2020-12Bitstream-Based Neural Network for Scalable, Efficient, and Accurate Deep Learning HardwareSim, Hyeonuk; Lee, JongeunARTICLE19
2019-09Cost-effective stochastic MAC circuits for deep neural networksSim, Hyeonuk; Lee, JongeunARTICLE226
2018-06-24DPS: Dynamic Precision Scaling for Stochastic Computing-Based Deep Neural NetworksSim, Hyeonuk; Kenzhegulov, Saken; Lee, JongeunCONFERENCE52
2016-08Efficient High-Level Synthesis for Nested Loops of Nonrectangular Iteration SpacesSim, Hyeonuk; Rahman, Atul; Lee, JongeunARTICLE478
2017-12-11FPGA Implementation of Convolutional Neural Network Based on Stochastic ComputingKim, Daewoo; Moghaddam, Mansureh S.; Moradian, Hossein; Sim, Hyeonuk; Lee, Jongeun; Choi, KiyoungCONFERENCE38
2019-01-21Log-quantized stochastic computing for memory and computation efficient DNNsSim, Hyeonuk; Lee, JongeunCONFERENCE46
2016-07Mapping imperfect loops to coarse-grained reconfigurable architecturesSim, Hyeonuk; Lee, Hongsik; Seo, Seongseok; Lee, JongeunARTICLE793
2017-01-16Scalable stochastic-computing accelerator for convolutional neural networksSim, Hyeonuk; Nguyen, Dong; Lee, Jongeun; Choi, KiyoungCONFERENCE52
2018-06-24Sign-Magnitude SC: Getting 10X Accuracy for Free in Stochastic Computing for Deep Neural NetworksZhakatayev, Aidyn; Lee, Sugil; Sim, Hyeonuk; Lee, JongeunCONFERENCE52
2020-08-10SparTANN: Sparse Training Accelerator for Neural Networks with Threshold-based SparsificationSim, Hyeonuk; Choi, Jooyeon; Lee, JongeunCONFERENCE16
2019-06-02Successive log quantization for cost-efficient neural networks using stochastic computingLee, Sugil; Sim, Hyeonuk; Choi, Jooyeon; Lee, JongeunCONFERENCE61
2019-01-21XoMA: Exclusive on-chip memory architecture for energy-efficient deep learning accelerationSim, Hyeonuk; Anderson, Jason H.; Lee, JongeunCONFERENCE47
Showing results 1 to 13 of 13

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