Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware
Simon F Muller-Cleve, Vittorio Fra, Lyes Khacef, Alejandro, Pequeno-Zurro, Daniel Klepatsch, Evelina Forno, Diego G Ivanovich, Shavika, Rastogi, Gianvito Urgese, Friedemann Zenke, Chiara Bartolozzi

TL;DR
This paper introduces a new benchmark for tactile spatio-temporal pattern recognition using Braille reading, comparing neuromorphic and traditional AI hardware, highlighting energy efficiency and accuracy trade-offs.
Contribution
It presents a novel Braille dataset, evaluates SNNs on neuromorphic hardware, and compares their performance to LSTMs on GPUs for edge tactile sensing applications.
Findings
LSTM achieves ~97% accuracy, outperforming SNN by ~17%.
Recurrent SNN on Loihi is ~500 times more energy-efficient than LSTM on Jetson.
Event-based encoding impacts the accuracy and efficiency of spike-based computation.
Abstract
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…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Ferroelectric and Negative Capacitance Devices
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory
