| Envision: A 0.26-to-10 TOPS/W Subword-Parallel Dynamic-Voltage-Accuracy-Frequency-Scalable Convolutional Neural Network Processor in 28nm FDSOI B Moons, R Uytterhoeven, W Dehaene, M Verhelst IEEE International Solid-State Circuits Conference (ISSCC), 246-257, 2017 | 420 | 2017 |
| An Always-On 3.8 J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS D Bankman, L Yang, B Moons, M Verhelst, B Murmann IEEE Journal of Solid-State Circuits 54 (1), 158-172, 2018 | 269 | 2018 |
| A 0.3–2.6 TOPS/W precision-scalable processor for real-time large-scale ConvNets B Moons, M Verhelst 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits), 1-2, 2016 | 198 | 2016 |
| An energy-efficient precision-scalable ConvNet processor in 40-nm CMOS B Moons, M Verhelst IEEE Journal of solid-state Circuits 52 (4), 903-914, 2016 | 170 | 2016 |
| Embedded deep neural network processing: Algorithmic and processor techniques bring deep learning to iot and edge devices M Verhelst, B Moons IEEE Solid-State Circuits Magazine 9 (4), 55-65, 2017 | 148 | 2017 |
| Minimum Energy Quantized Neural Networks B Moons, K Goetschalckx, N Van Berckelaer, M Verhelst Signals, Systems, and Computers, 2017 51st Asilomar Conference on, 2017 | 127 | 2017 |
| Energy-Efficient ConvNets through Approximate Computing B Moons, B De Brabandere, L Van Gool, M Verhelst IEEE international winter Conference on applications of computer vision (WACV), 2016 | 121 | 2016 |
| BinarEye: An always-on energy-accuracy-scalable binary CNN processor with all memory on chip in 28nm CMOS B Moons, D Bankman, L Yang, B Murmann, M Verhelst 2018 IEEE Custom Integrated Circuits Conference (CICC), 1-4, 2018 | 101 | 2018 |
| Energy-efficiency and accuracy of stochastic computing circuits in emerging technologies B Moons, M Verhelst IEEE Journal on Emerging and Selected Topics in Circuits and Systems 4 (4 …, 2014 | 80 | 2014 |
| Dvas: Dynamic voltage accuracy scaling for increased energy-efficiency in approximate computing B Moons, M Verhelst 2015 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2015 | 69 | 2015 |
| Embedded Deep Learning B Moons, D Bankman, M Verhelst | 56 | 2019 |
| DVAFS: Trading computational accuracy for energy through dynamic-voltage-accuracy-frequency-scaling B Moons, R Uytterhoeven, W Dehaene, M Verhelst Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017 …, 2017 | 48 | 2017 |
| Bit error tolerance of a CIFAR-10 binarized convolutional neural network processor L Yang, D Bankman, B Moons, M Verhelst, B Murmann 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2018 | 42 | 2018 |
| Distilling optimal neural networks: Rapid search in diverse spaces B Moons, P Noorzad, A Skliar, G Mariani, D Mehta, C Lott, T Blankevoort International Conference on Computer Vision (ICCV), 12229-12238, 2021 | 32 | 2021 |
| Optimized hierarchical cascaded processing K Goetschalckx, B Moons, S Lauwereins, M Andraud, M Verhelst IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8 (4 …, 2018 | 30 | 2018 |
| Efficiently combining svd, pruning, clustering and retraining for enhanced neural network compression K Goetschalckx, B Moons, P Wambacq, M Verhelst Proceedings of the 2nd International Workshop on Embedded and Mobile Deep …, 2018 | 14 | 2018 |
| TRIG: Hardware accelerator for inference-based applications and experimental demonstration using carbon nanotube FETs G Hills, D Bankman, B Moons, L Yang, J Hillard, A Kahng, R Park, ... Proceedings of the 55th Annual Design Automation Conference, 1-10, 2018 | 9 | 2018 |
| Energy and accuracy in multi-stage stochastic computing B Moons, M Verhelst 2014 IEEE 12th International New Circuits and Systems Conference (NEWCAS …, 2014 | 8 | 2014 |
| Circuit techniques for approximate computing B Moons, D Bankman, M Verhelst, B Moons, D Bankman, M Verhelst Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on …, 2019 | 6 | 2019 |
| Embedded deep neural networks B Moons, D Bankman, M Verhelst, B Moons, D Bankman, M Verhelst Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on …, 2019 | 3 | 2019 |
Marian VerhelstMicas - ESAT - KU Leuven, BelgiumVerified email at esat.kuleuven.be
