Bert Moons

发表

Conclusions, Contributions, and Future Work

Marian Verhelst, Bert Moons, Daniel Bankman, 2019 .

Optimized Hierarchical Cascaded Processing

Marian Verhelst, Steven Lauwereins, Bert Moons, 2018, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

TRIG: hardware accelerator for inference-based applications and experimental demonstration using carbon nanotube FETs

Marian Verhelst, H.-S. Philip Wong, Boris Murmann, 2018, DAC.

A 0.3–2.6 TOPS/W precision-scalable processor for real-time large-scale ConvNets

Marian Verhelst, Bert Moons, M. Verhelst, 2016, 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits).

ENVISION: Energy-Scalable Sparse Convolutional Neural Network Processing

Marian Verhelst, Bert Moons, Daniel Bankman, 2019 .

Embedded Deep Neural Network Processing: Algorithmic and Processor Techniques Bring Deep Learning to IoT and Edge Devices

Marian Verhelst, Bert Moons, M. Verhelst, 2017, IEEE Solid-State Circuits Magazine.

Minimum energy quantized neural networks

Marian Verhelst, Bert Moons, Koen Goetschalckx, 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

Efficiently Combining SVD, Pruning, Clustering and Retraining for Enhanced Neural Network Compression

Marian Verhelst, Patrick Wambacq, Bert Moons, 2018, EMDL@MobiSys.

Energy and accuracy in multi-stage stochastic computing

Marian Verhelst, Bert Moons, 2014, 2014 IEEE 12th International New Circuits and Systems Conference (NEWCAS).

TRIG: Hardware Accelerator for Inference-Based Applications and Experimental Demonstration Using Carbon Nanotube FETs

Marian Verhelst, Boris Murmann, Subhasish Mitra, 2018, 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC).

An Always-On 3.8 $\mu$ J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS

Marian Verhelst, Boris Murmann, Bert Moons, 2019, IEEE Journal of Solid-State Circuits.

Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces pdf

Bert Moons, Parham Noorzad, Giovanni Mariani, 2020, ArXiv.

14.5 Envision: A 0.26-to-10TOPS/W subword-parallel dynamic-voltage-accuracy-frequency-scalable Convolutional Neural Network processor in 28nm FDSOI

Marian Verhelst, Wim Dehaene, Bert Moons, 2017, 2017 IEEE International Solid-State Circuits Conference (ISSCC).

An always-on 3.8μJ/86% CIFAR-10 mixed-signal binary CNN processor with all memory on chip in 28nm CMOS

Marian Verhelst, Boris Murmann, Bert Moons, 2018, 2018 IEEE International Solid - State Circuits Conference - (ISSCC).

Embedded Deep Learning

Bert Moons, Daniel Bankman, Marian Verhelst, 2018 .

Energy-Efficiency and Accuracy of Stochastic Computing Circuits in Emerging Technologies

Marian Verhelst, Bert Moons, 2014, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

Hardware-Algorithm Co-optimizations

Marian Verhelst, Bert Moons, Daniel Bankman, 2018, Embedded Deep Learning.

BINAREYE: Digital and Mixed-Signal Always-On Binary Neural Network Processing

Marian Verhelst, Bert Moons, Daniel Bankman, 2019 .

DVAFS: Trading computational accuracy for energy through dynamic-voltage-accuracy-frequency-scaling

Marian Verhelst, Wim Dehaene, Bert Moons, 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

DVAS: Dynamic Voltage Accuracy Scaling for increased energy-efficiency in approximate computing

Marian Verhelst, Bert Moons, M. Verhelst, 2015, 2015 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED).

Energy-efficient ConvNets through approximate computing

Marian Verhelst, Luc Van Gool, Bert Moons, 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

DVAFS—Dynamic-Voltage-Accuracy-Frequency-Scaling Applied to Scalable Convolutional Neural Network Acceleration

Bert Moons, Marian Verhelst, 2020 .

Embedded Deep Neural Networks

Marian Verhelst, Bert Moons, Daniel Bankman, 2018, Embedded Deep Learning.

Bit Error Tolerance of a CIFAR-10 Binarized Convolutional Neural Network Processor

Marian Verhelst, Boris Murmann, Bert Moons, 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

The influence of Spatial and Transient Circuit Variations on Energy and Accuracy in Stochastic Computing Circuits

Marian Verhelst, Bert Moons, 2014 .

BinarEye: An always-on energy-accuracy-scalable binary CNN processor with all memory on chip in 28nm CMOS

Boris Murmann, Bert Moons, Daniel Bankman, 2018, 2018 IEEE Custom Integrated Circuits Conference (CICC).

An Energy-Efficient Precision-Scalable ConvNet Processor in 40-nm CMOS

Marian Verhelst, Bert Moons, M. Verhelst, 2017, IEEE Journal of Solid-State Circuits.

Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion pdf

Marian Verhelst, Matthijs Van Keirsbilck, Bert Moons, 2018, ArXiv.

Circuit Techniques for Approximate Computing

Marian Verhelst, Bert Moons, Daniel Bankman, 2018, Embedded Deep Learning.

5 ENVISION : A 0 . 26-to-10 TOPS / W Subword-Parallel Dynamic-Voltage-Accuracy-Frequency-Scalable Convolutional Neural Network Processor in 28 nm FDSOI

Marian Verhelst, Wim Dehaene, Bert Moons, 2017 .