Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
This addresses memory constraints in scalable neuromorphic systems, offering a more efficient alternative to existing methods, though it is incremental in improving hardware implementation.
The paper tackles the memory inefficiency of implementing spike-timing-dependent plasticity (STDP) in neuromorphic hardware by proposing a method that uses only forward lookup access to the synaptic connectivity table, achieving substantial memory savings for sparsely connected networks.
Spike-timing-dependent plasticity (STDP) incurs both causal and acausal synaptic weight updates, for negative and positive time differences between pre-synaptic and post-synaptic spike events. For realizing such updates in neuromorphic hardware, current implementations either require forward and reverse lookup access to the synaptic connectivity table, or rely on memory-intensive architectures such as crossbar arrays. We present a novel method for realizing both causal and acausal weight updates using only forward lookup access of the synaptic connectivity table, permitting memory-efficient implementation. A simplified implementation in FPGA, using a single timer variable for each neuron, closely approximates exact STDP cumulative weight updates for neuron refractory periods greater than 10 ms, and reduces to exact STDP for refractory periods greater than the STDP time window. Compared to conventional crossbar implementation, the forward table-based implementation leads to substantial memory savings for sparsely connected networks supporting scalable neuromorphic systems with fully reconfigurable synaptic connectivity and plasticity.