Interspike-Interval-Based Analog Spike-Time-Dependent Encoder for Neuromorphic Processors

Von Neumann bottleneck, which refers to the limited throughput between the CPU and memory, has already become a major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems have started to gain the increasing attentions as compact and energy-efficient computing platforms. As one of the most crucial components in the neuromorphic computing systems, neural encoder transforms the stimulus (input signals) into spike trains. In this paper, we adapt the temporal encoding scheme of interspike intervals (ISIs) and present an analog temporal neural encoder with its verification and recovery schemes. The proposed neural encoder allows efficient mapping of signal amplitude information into a spike-time sequence that represents the input data and offers perfect recovery for band-limited stimuli. With the novel iterative structure, the number of spikes increases exponentially with the number of neurons. From the measurements obtained from the fabricated neural encoder chip, our temporal encoder with ISI encoding is proved to be robust and error tolerant.