Bert Moons
Bert Moons
Qualcomm AI Research
Verified email at qti.qualcomm.com
Title
Cited by
Cited by
Year
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
2282017
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
1312016
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
1212018
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
862016
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
762016
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
602017
Minimum Energy Quantized Neural Networks
B Moons, K Goetschalckx, N Van Berckelaer, M Verhelst
Signals, Systems, and Computers, 2017 51st Asilomar Conference on, 2017
572017
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
552014
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
412015
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
362018
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
292017
Embedded Deep Learning
B Moons, D Bankman, M Verhelst
132019
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
122018
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
102018
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
62018
Energy and accuracy in multi-stage stochastic computing
B Moons, M Verhelst
2014 IEEE 12th International New Circuits and Systems Conference (NEWCAS …, 2014
62014
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
52018
Embedded Deep Neural Networks
B Moons, D Bankman, M Verhelst
Embedded Deep Learning, 1-31, 2019
22019
Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion
M Van keirsbilck, B Moons, M Verhelst
arXiv preprint arXiv:1803.04840, 2018
22018
Circuit Techniques for Approximate Computing
B Moons, D Bankman, M Verhelst
Embedded Deep Learning, 89-113, 2019
12019
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Articles 1–20