Search Results for author: Marian Verhelst

Found 11 papers, 5 papers with code

Delta Keyword Transformer: Bringing Transformers to the Edge through Dynamically Pruned Multi-Head Self-Attention

no code implementations20 Mar 2022 Zuzana Jelčicová, Marian Verhelst

Moreover, a reduction of ~87-94% operations can be achieved when only degrading the accuracy by 1-4%, speeding up the multi-head self-attention inference by a factor of ~7. 5-16.

Keyword Spotting

ProbLP: A framework for low-precision probabilistic inference

1 code implementation27 Feb 2021 Nimish Shah, Laura I. Galindez Olascoaga, Wannes Meert, Marian Verhelst

Bayesian reasoning is a powerful mechanism for probabilistic inference in smart edge-devices.

Feed-Forward On-Edge Fine-tuning Using Static Synthetic Gradient Modules

no code implementations21 Sep 2020 Robby Neven, Marian Verhelst, Tinne Tuytelaars, Toon Goedemé

By first training the SGMs in a meta-learning manner on a set of common objects, during fine-tuning, the SGMs provided the model with accurate gradients to successfully learn to grasp new objects.


ZigZag: A Memory-Centric Rapid DNN Accelerator Design Space Exploration Framework

1 code implementation22 Jul 2020 Linyan Mei, Pouya Houshmand, Vikram Jain, Sebastian Giraldo, Marian Verhelst

This work introduces ZigZag, a memory-centric rapid DNN accelerator DSE framework which extends the DSE with uneven mapping opportunities, in which operands at shared memory levels are no longer bound to use the same memory levels for each loop index.

Distributed, Parallel, and Cluster Computing C.1.4; C.3; C.4

Benchmarking TinyML Systems: Challenges and Direction

1 code implementation10 Mar 2020 Colby R. Banbury, Vijay Janapa Reddi, Max Lam, William Fu, Amin Fazel, Jeremy Holleman, Xinyuan Huang, Robert Hurtado, David Kanter, Anton Lokhmotov, David Patterson, Danilo Pau, Jae-sun Seo, Jeff Sieracki, Urmish Thakker, Marian Verhelst, Poonam Yadav

In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads.

Towards Hardware-Aware Tractable Learning of Probabilistic Models

1 code implementation NeurIPS 2019 Laura I. Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van Den Broeck

We showcase our framework on a mobile activity recognition scenario, and on a variety of benchmark datasets representative of the field of tractable learning and of the applications of interest.

Activity Recognition Edge-computing +1

A multi-layered energy consumption model for smart wireless acoustic sensor networks

1 code implementation17 Dec 2018 Gert Dekkers, Fernando Rosas, Steven Lauwereins, Sreeraj Rajendran, Sofie Pollin, Bart Vanrumste, Toon van Waterschoot, Marian Verhelst, Peter Karsmakers

This model provides a first step of exploration prior to custom design of a smart wireless acoustic sensor, and also can be used to compare the energy consumption of different protocols.

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

no code implementations13 Mar 2018 Matthijs Van keirsbilck, Bert Moons, Marian Verhelst

Performing multi-modal speech recognition - processing acoustic speech signals and lip-reading video simultaneously - significantly enhances the performance of such systems, especially in noisy environments.

Automatic Speech Recognition Lip Reading

Minimum Energy Quantized Neural Networks

no code implementations1 Nov 2017 Bert Moons, Koen Goetschalckx, Nick Van Berckelaer, Marian Verhelst

To this end, the energy consumption of inference is modeled for a generic hardware platform.

Energy-Efficient ConvNets Through Approximate Computing

no code implementations22 Mar 2016 Bert Moons, Bert de Brabandere, Luc van Gool, Marian Verhelst

Recently ConvNets or convolutional neural networks (CNN) have come up as state-of-the-art classification and detection algorithms, achieving near-human performance in visual detection.

Classification General Classification

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