ETLP: Event-based Three-factor Local Plasticity for online learning with
neuromorphic hardware
- URL: http://arxiv.org/abs/2301.08281v1
- Date: Thu, 19 Jan 2023 19:45:42 GMT
- Title: ETLP: Event-based Three-factor Local Plasticity for online learning with
neuromorphic hardware
- Authors: Fernando M. Quintana, Fernando Perez-Pe\~na, Pedro L. Galindo, Emre O.
Netfci, Elisabetta Chicca, Lyes Khacef
- Abstract summary: We show a competitive performance in accuracy with a clear advantage in the computational complexity for Event-Based Three-factor Local Plasticity (ETLP)
We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learntemporal patterns with a rich temporal structure.
- Score: 105.54048699217668
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Neuromorphic perception with event-based sensors, asynchronous hardware and
spiking neurons is showing promising results for real-time and energy-efficient
inference in embedded systems. The next promise of brain-inspired computing is
to enable adaptation to changes at the edge with online learning. However, the
parallel and distributed architectures of neuromorphic hardware based on
co-localized compute and memory imposes locality constraints to the on-chip
learning rules. We propose in this work the Event-Based Three-factor Local
Plasticity (ETLP) rule that uses (1) the pre-synaptic spike trace, (2) the
post-synaptic membrane voltage and (3) a third factor in the form of projected
labels with no error calculation, that also serve as update triggers. We apply
ETLP with feedforward and recurrent spiking neural networks on visual and
auditory event-based pattern recognition, and compare it to Back-Propagation
Through Time (BPTT) and eProp. We show a competitive performance in accuracy
with a clear advantage in the computational complexity for ETLP. We also show
that when using local plasticity, threshold adaptation in spiking neurons and a
recurrent topology are necessary to learn spatio-temporal patterns with a rich
temporal structure. Finally, we provide a proof of concept hardware
implementation of ETLP on FPGA to highlight the simplicity of its computational
primitives and how they can be mapped into neuromorphic hardware for online
learning with low-energy consumption and real-time interaction.
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