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arXiv 2021年

作者： Glover, Arren Dinale, Aiko Rosa, Leandro De Souza Bamford, Simeon Bartolozzi, Chiara Event-Driven Perception for Robotics group Istituto Italiano di Tecnologia Italy

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel "threshold ordinal event-surface" that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only "as-fast-as-possible", i.e. only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than 2.6x the speed of current state-of-the-art;a necessity when using high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras. © 2021, CC BY.

关键词： Cameras

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Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware

arXiv

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arXiv 2022年

作者： Müller-Cleve, Simon F. Fra, Vittorio Khacef, Lyes Pequeño-Zurro, Alejandro Klepatsch, Daniel Forno, Evelina Ivanovich, Diego G. Rastogi, Shavika Urgese, Gianvito Zenke, Friedemann Bartolozzi, Chiara Istituto Italiano di Tecnologia Event-driven Perception in Robotics Genoa Italy Politecnico di Torino EDA Group Torino Italy Bio-Inspired Circuits and Systems Lab Zernike Institute for Advanced Materials Groningen Cognitive Systems and Materials Center University of Groningen Groningen Netherlands Laboratory of Neural Computation Istituto Italiano di Tecnologia Genova Italy Silicon Austria Labs JKU LIT SAL eSPML Lab Austria Johannes Kepler University Linz JKU LIT SAL eSPML Lab Austria International Centre for Neuromorphic Systems Western Sydney University Australia Biocomputation Research Group University of Hertfordshire United Kingdom Friedrich Miescher Institute for Biomedical Research Neurobiology Basel Switzerland University of Basel Basel Switzerland

Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic applications is a representative example where real-time processing and energy efficiency are required. Following a brain-inspired computing approach, we propose a new benchmark for spatio-temporal tactile pattern recognition at the edge through Braille letter reading. We recorded a new Braille letters dataset based on the capacitive tactile sensors of the iCub robot's fingertip. We then investigated the importance of spatial and temporal information as well as the impact of event-based encoding on spike-based computation. Afterward, we trained and compared feedforward and recurrent Spiking Neural Networks (SNNs) offline using Backpropagation Through Time (BPTT) with surrogate gradients, then we deployed them on the Intel Loihi neuromorphic chip for fast and efficient inference. We compared our approach to standard classifiers, in particular to the Long Short-Term Memory (LSTM) deployed on the embedded NVIDIA Jetson GPU, in terms of classification accuracy, power, and energy consumption together with computational delay. Our results show that the LSTM reaches ∼97% of accuracy, outperforming the recurrent SNN by ∼17 % when using continuous frame-based data instead of event-based inputs. However, the recurrent SNN on Loihi with event-based inputs is ∼500 times more energy-efficient than the LSTM on Jetson, requiring a total power of only ∼30 mW. This work proposes a new benchmark for tactile sensing and highlights the challenges and opportunities of event-based encoding, neuromorphic hardware, and spike-based computing for spatio-temporal pattern recognition at the edge. Copyright © 2022, The Authors. All rights reserved.

关键词： Long short-term memory

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