Fig. 5
Experimental demonstration of multi-memristive synapses used in a spiking neural network. a A spiking neural network is trained to perform the task of temporal correlation detection through unsupervised learning. Our network consists of 1000 multi-PCM synapses (in hardware) connected to one integrate-and-fire (I&F) software neuron. The synapses receive event-based data streams generated with Poisson distributions as presynaptic input spikes. 100 of the synapses receive correlated data streams with a correlation coefficient of 0.75, whereas the rest of the synapses receive uncorrelated data streams. The correlated and the uncorrelated data streams both have the same rate. The resulting postsynaptic outputs are accumulated at the neuronal membrane. The neuron fires, i.e., sends an output spike, if the membrane potential exceeds a threshold. The weight update amount is calculated using an exponential STDP rule based on the timing of the input spikes and the neuronal spikes. A potentiation (depression) pulse with fixed amplitude is applied if the desired weight change is higher (lower) than a threshold. b The synaptic weights are shown for synapses comprising N = 1, 3, and 7 PCM devices at the end of the experiment (5000 time steps). It can be seen that the weights of the synapses receiving correlated inputs tend to be larger than the weights of those receiving uncorrelated inputs. The weight distribution shows a clearer separation with increasing N. c Weight evolution of six synapses in the first 300 time steps of the experiment. The weight evolves more gradually with the number of devices per synapse. d Synaptic weight distribution of an SNN comprising 144,000 multi-PCM synapses with N = 7 PCM devices at the end of an experiment (3000 time steps) (upper panel). 14,400 synapses receive correlated input data streams with a correlation coefficient of 0.75. A total of 1,008,000 PCM devices are used for this large-scale experiment. The lower panel shows the synaptic weight distribution predicted by the PCM device model
