Search Results for author: Lukas Cavigelli

Found 29 papers, 12 papers with code

Sub-100uW Multispectral Riemannian Classification for EEG-based Brain--Machine Interfaces

no code implementations18 Dec 2021 Xiaying Wang, Lukas Cavigelli, Tibor Schneider, Luca Benini

Motor imagery brain--machine interfaces enable us to control machines by merely thinking of performing a motor action.

EEG

Reinforcement Learning for Scalable Logic Optimization with Graph Neural Networks

no code implementations4 May 2021 Xavier Timoneda, Lukas Cavigelli

Logic optimization is an NP-hard problem commonly approached through hand-engineered heuristics.

ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network

1 code implementation25 Mar 2021 Thorir Mar Ingolfsson, Xiaying Wang, Michael Hersche, Alessio Burrello, Lukas Cavigelli, Luca Benini

With 9. 91 GMAC/s/W, it is 23. 0 times more energy-efficient and 46. 85 times faster than an implementation on the ARM Cortex M4F (0. 43 GMAC/s/W).

Arrhythmia Detection

Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices

no code implementations12 Jan 2021 Gianmarco Cerutti, Renzo Andri, Lukas Cavigelli, Michele Magno, Elisabetta Farella, Luca Benini

This BNN reaches a 77. 9% accuracy, just 7% lower than the full-precision version, with 58 kB (7. 2 times less) for the weights and 262 kB (2. 4 times less) memory in total.

Event Detection Object Recognition +2

CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with Better-than-Binary Energy Efficiency

no code implementations3 Nov 2020 Moritz Scherer, Georg Rutishauser, Lukas Cavigelli, Luca Benini

We present a 3. 1 POp/s/W fully digital hardware accelerator for ternary neural networks.

Hardware Architecture

ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator

no code implementations12 May 2020 Renzo Andri, Geethan Karunaratne, Lukas Cavigelli, Luca Benini

Furthermore, it can perform inference on a binarized ResNet-18 trained with 8-bases Group-Net to achieve a 67. 5% Top-1 accuracy with only 3. 0 mJ/frame -- at an accuracy drop of merely 1. 8% from the full-precision ResNet-18.

Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine Interfaces

1 code implementation24 Apr 2020 Tibor Schneider, Xiaying Wang, Michael Hersche, Lukas Cavigelli, Luca Benini

We quantize weights and activations to 8-bit fixed-point with a negligible accuracy loss of 0. 4% on 4-class MI, and present an energy-efficient hardware-aware implementation on the Mr. Wolf parallel ultra-low power (PULP) System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core compute cluster.

EEG

RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks

no code implementations4 Jan 2020 Lukas Cavigelli, Luca Benini

We present Random Partition Relaxation (RPR), a method for strong quantization of neural networks weight to binary (+1/-1) and ternary (+1/0/-1) values.

Quantization

HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data

no code implementations10 Dec 2019 Xiaying Wang, Lukas Cavigelli, Manuel Eggimann, Michele Magno, Luca Benini

Synthetic aperture radar (SAR) data is becoming increasingly available to a wide range of users through commercial service providers with resolutions reaching 0. 5m/px.

Scene Segmentation

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

1 code implementation8 Nov 2019 Xiaying Wang, Michele Magno, Lukas Cavigelli, Luca Benini

The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network.

Edge-computing

EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators

2 code implementations30 Aug 2019 Lukas Cavigelli, Georg Rutishauser, Luca Benini

In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly.

Image Classification Object Recognition +1

Extended Bit-Plane Compression for Convolutional Neural Network Accelerators

1 code implementation1 Oct 2018 Lukas Cavigelli, Luca Benini

After the tremendous success of convolutional neural networks in image classification, object detection, speech recognition, etc., there is now rising demand for deployment of these compute-intensive ML models on tightly power constrained embedded and mobile systems at low cost as well as for pushing the throughput in data centers.

Image Classification Object Detection +1

CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams

2 code implementations15 Aug 2018 Lukas Cavigelli, Luca Benini

The last few years have brought advances in computer vision at an amazing pace, grounded on new findings in deep neural network construction and training as well as the availability of large labeled datasets.

Object Detection Semantic Segmentation

Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

2 code implementations18 Jun 2018 Michael Hersche, Tino Rellstab, Pasquale Davide Schiavone, Lukas Cavigelli, Luca Benini, Abbas Rahimi

Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems.

EEG

Design Automation for Binarized Neural Networks: A Quantum Leap Opportunity?

no code implementations21 Nov 2017 Manuele Rusci, Lukas Cavigelli, Luca Benini

Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN).

Efficient Convolutional Neural Network For Audio Event Detection

no code implementations28 Sep 2017 Matthias Meyer, Lukas Cavigelli, Lothar Thiele

Wireless distributed systems as used in sensor networks, Internet-of-Things and cyber-physical systems, impose high requirements on resource efficiency.

Event Detection General Classification

CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data

1 code implementation14 Apr 2017 Lukas Cavigelli, Philippe Degen, Luca Benini

Extracting per-frame features using convolutional neural networks for real-time processing of video data is currently mainly performed on powerful GPU-accelerated workstations and compute clusters.

CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression

1 code implementation22 Nov 2016 Lukas Cavigelli, Pascal Hager, Luca Benini

Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media.

Image Compression

Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks

no code implementations9 Nov 2016 Lukas Cavigelli, Dominic Bernath, Michele Magno, Luca Benini

The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats.

General Classification Scene Labeling

Deep Structured Features for Semantic Segmentation

no code implementations26 Sep 2016 Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski, Luca Benini

We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms.

General Classification Semantic Segmentation

YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration

no code implementations17 Jun 2016 Renzo Andri, Lukas Cavigelli, Davide Rossi, Luca Benini

Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy.

General Classification Image Classification

Origami: A 803 GOp/s/W Convolutional Network Accelerator

no code implementations14 Dec 2015 Lukas Cavigelli, Luca Benini

An ever increasing number of computer vision and image/video processing challenges are being approached using deep convolutional neural networks, obtaining state-of-the-art results in object recognition and detection, semantic segmentation, action recognition, optical flow and superresolution.

Action Recognition Object Recognition +2

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