  Neuromorphic systems that learn and predict from streaming inputs hold
significant promise in pervasive edge computing and its applications. In this
paper, a neuromorphic system that processes spatio-temporal information on the
edge is proposed. Algorithmically, the system is based on hierarchical temporal
memory that inherently offers online learning, resiliency, and fault tolerance.
Architecturally, it is a full custom mixed-signal design with an underlying
digital communication scheme and analog computational modules. Therefore, the
proposed system features reconfigurability, real-time processing, low power
consumption, and low-latency processing. The proposed architecture is
benchmarked to predict on real-world streaming data. The network's mean
absolute percentage error on the mixed-signal system is 1.129X lower compared
to its baseline algorithm model. This reduction can be attributed to device
non-idealities and probabilistic formation of synaptic connections. We
demonstrate that the combined effect of Hebbian learning and network sparsity
also plays a major role in extending the overall network lifespan. We also
illustrate that the system offers 3.46X reduction in latency and 77.02X
reduction in power consumption when compared to a custom CMOS digital design
implemented at the same technology node. By employing specific low power
techniques, such as clock gating, we observe 161.37X reduction in power
consumption.
