Chunking is a computational principle essential for memory compression, structural decomposition, and predictive processing. Humans seamlessly group perceptual sequences in units of chunks, parsed and memorized as separate entities. On an algorithmic level, computational models such as the Hierarchical Chunking Model (HCM) propose grouping proximal observational units as chunks, which resemble human chunk learning.Here we propose a biologically plausible and highly efficient implementation of the HCM: the neuromorphic HCM (nHCM).When parsing through perceptual sequences, the nHCM uses sparsely connected spiking neurons to construct hierarchical chunk representations in an event-driven way.Even when simulated on a standard computer, the nHCM showed remarkable improvement in speed, power consumption, and memory usage compared to its original counterpart.Then, we validate the model on mixed-signal neuromorphic hardware using recurrent spiking neural networks (SNN) with biologically plausible dynamics. We verified the robust computing properties of this implementation, overcoming the heterogeneity, variability, and low precision of the bio-plausible electronic analog circuits. With a successful implementation on both computers and neuromorphic processors, we show that the algorithm, and in general the neuromorphic co-design paradigm, is inherently efficient and robust. This work demonstrates cognitively-plausible sequence learning in energy-efficient dedicated neural computing electronic processing systems.