Search Results for author: Marian Verhelst

Found 16 papers, 6 papers with code

Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference

no code implementations8 Jun 2023 Matteo Risso, Alessio Burrello, Giuseppe Maria Sarda, Luca Benini, Enrico Macii, Massimo Poncino, Marian Verhelst, Daniele Jahier Pagliari

The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a diverse set of hardware accelerators.

Quantization

SALSA: Simulated Annealing based Loop-Ordering Scheduler for DNN Accelerators

1 code implementation20 Apr 2023 Victor J. B. Jung, Arne Symons, Linyan Mei, Marian Verhelst, Luca Benini

To meet the growing need for computational power for DNNs, multiple specialized hardware architectures have been proposed.

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

1 code implementation10 Apr 2023 Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp Stratmann, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi

The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.

Benchmarking

Real-Time Acoustic Perception for Automotive Applications

no code implementations30 Jan 2023 Jun Yin, Stefano Damiano, Marian Verhelst, Toon van Waterschoot, Andre Guntoro

On the algorithmic side, the I-SPOT Project aims to enable detecting, localizing and tracking environmental audio signals by jointly developing microphone array processing and deep learning techniques that specifically target automotive applications.

Hardware-aware mobile building block evaluation for computer vision

no code implementations26 Aug 2022 Maxim Bonnaerens, Matthias Freiberger, Marian Verhelst, Joni Dambre

In this work we propose a methodology to accurately evaluate and compare the performance of efficient neural network building blocks for computer vision in a hardware-aware manner.

Benchmarking Efficient Neural Network

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.

Meta-Learning

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

no code implementations22 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

2 code implementations10 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.

Benchmarking Position

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 Automatic Speech Recognition (ASR) +3

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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