| Envision: A 0.26-to-10TOPS/W subword-parallel dynamic-voltage-accuracy-frequency-scalable Convolutional Neural Network processor in 28nm FDSOI B Moons, R Uytterhoeven, W Dehaene, M Verhelst Solid-State Circuits Conference (ISSCC), 2017 IEEE International, 246-247, 2017 | 543* | 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 | 355 | 2018 |
| A review on Internet of Things solutions for intelligent energy control in buildings for smart city applications I Khajenasiri, A Estebsari, M Verhelst, G Gielen Energy Procedia 111, 770-779, 2017 | 316 | 2017 |
| Wireless communication technology, apparatuses, and methods E Alpman, AL Amadjikpe, O Asaf, K Azadet, R Banin, M Baryakh, A Bazov, ... US Patent 11,424,539, 2022 | 304 | 2022 |
| Benchmarking tinyml systems: Challenges and direction CR Banbury, VJ Reddi, M Lam, W Fu, A Fazel, J Holleman, X Huang, ... arXiv preprint arXiv:2003.04821, 2020 | 300 | 2020 |
| 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 | 230 | 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 | 212 | 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 | 198 | 2017 |
| Minimum energy quantized neural networks B Moons, K Goetschalckx, N Van Berckelaer, M Verhelst 2017 51st Asilomar Conference on Signals, Systems, and Computers, 1921-1925, 2017 | 182 | 2017 |
| Energy-efficient convnets through approximate computing B Moons, B De Brabandere, L Van Gool, M Verhelst 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 1-8, 2016 | 149 | 2016 |
| The SINS Database for Detection of Daily Activities in a Home Environment Using an Acoustic Sensor Network. G Dekkers, S Lauwereins, B Thoen, MW Adhana, H Brouckxon, ... DCASE, 32-36, 2017 | 147 | 2017 |
| ZigZag: Enlarging joint architecture-mapping design space exploration for DNN accelerators L Mei, P Houshmand, V Jain, S Giraldo, M Verhelst IEEE Transactions on Computers 70 (8), 1160-1174, 2021 | 136 | 2021 |
| A 90 nm CMOS, $6\{\upmu {\text {W}}} $ Power-Proportional Acoustic Sensing Frontend for Voice Activity Detection KMH Badami, S Lauwereins, W Meert, M Verhelst IEEE Journal of Solid-State Circuits 51 (1), 291-302, 2015 | 134 | 2015 |
| 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 | 130 | 2018 |
| A 172µW compressive sampling photoplethysmographic readout with embedded direct heart-rate and variability extraction from compressively sampled data VR Pamula, JM Valero-Sarmiento, L Yan, A Bozkurt, C Van Hoof, ... IEEE Transactions on Biomedical Circuits and Systems 11 (3), 487-496, 2017 | 126* | 2017 |
| A 2.4-GHz 20–40-MHz channel WLAN digital outphasing transmitter utilizing a delay-based wideband phase modulator in 32-nm CMOS A Ravi, P Madoglio, H Xu, K Chandrashekar, M Verhelst, S Pellerano, ... IEEE Journal of Solid-State Circuits 47 (12), 3184-3196, 2012 | 114 | 2012 |
| Where analog meets digital: Analog? to? information conversion and beyond M Verhelst, A Bahai IEEE Solid-state circuits magazine 7 (3), 67-80, 2015 | 110 | 2015 |
| Vocell: A 65-nm Speech-Triggered Wake-Up SoC for 10- W Keyword Spotting and Speaker Verification JSP Giraldo, S Lauwereins, K Badami, M Verhelst IEEE Journal of Solid-State Circuits 55 (4), 868-878, 2020 | 100 | 2020 |
| Review and benchmarking of precision-scalable multiply-accumulate unit architectures for embedded neural-network processing V Camus, L Mei, C Enz, M Verhelst IEEE Journal on Emerging and Selected Topics in Circuits and Systems 9 (4 …, 2019 | 99 | 2019 |
| 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 | 88 | 2015 |
Bert MoonsAxelera AI, previously at Qualcomm AI Research and KU Leuvenaxelera.ai의 이메일 확인됨