3 research outputs found
Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware
Spatio-temporal pattern recognition is a fundamental ability of the brain
which is required for numerous real-world applications. Recent deep learning
approaches have reached outstanding accuracy 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 letters reading. We recorded a new braille letters dataset based on the
capacitive tactile sensors/fingertip of the iCub robot, then we investigated
the importance of temporal information and the impact of event-based encoding
for spike-based/event-based computation. Afterwards, we trained and compared
feed-forward and recurrent spiking neural networks (SNNs) offline using
back-propagation through time with surrogate gradients, then we deployed them
on the Intel Loihi neuromorphic chip for fast and efficient inference. We
confronted our approach to standard classifiers, in particular to a Long
Short-Term Memory (LSTM) deployed on the embedded Nvidia Jetson GPU in terms of
classification accuracy, power/energy consumption and computational delay. Our
results show that the LSTM outperforms the recurrent SNN in terms of accuracy
by 14%. However, the recurrent SNN on Loihi is 237 times more energy-efficient
than the LSTM on Jetson, requiring an average power of only 31mW. 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.Comment: 20 pages, submitted to Frontiers in Neuroscience - Neuromorphic
Engineerin