Analog computing at the edge is an emerging strategy to limit data storage and transmission requirements, as well as energy consumption, and its practical implementation is in its initial stages of development. Translating properties of biological neurons into hardware offers a pathway towards low-power, real-time edge processing. Specifically, resonator neurons offer selectivity to specific frequencies as a potential solution for temporal signal processing. Here, we show a fabricated Complementary Metal-Oxide-Semiconductor (CMOS) mixed-signal Resonate-and-Fire (R&F) neuron circuit implementation that emulates the behavior of these neural cells responsible for controlling oscillations within the central nervous system. We integrate the design with asynchronous handshake capabilities, perform comprehensive variability analyses, and characterize its frequency detection functionality. Our results demonstrate the feasibility of large-scale integration within neuromorphic systems, thereby advancing the exploitation of bio-inspired circuits for efficient edge temporal signal processing.

Attractor networks are widely understood to be a re-occurring primitive that underlies cognitive function. Stabilising activity in spiking attractor networks however remains a difficult task, especially when implemented in analog integrated circuits (aIC). We introduce here a novel circuit implementation of a hard Winner-Take-All (hWTA) mechanism, in which competing neurons' refractory circuits are coupled together, and thus their spiking is forced to be mutually exclusive. We demonstrate stable persistent-firing attractor dynamics in a small on-chip network consisting of hWTA-connected neurons and excitatory recurrent synapses. Its utility within larger networks is demonstrated in simulation, and shown to support overlapping attractors and be robust to synaptic weight mismatch. The realised hWTA mechanism is thus useful for stabilising activity in spiking networks composed of unreliable components, without the need for careful parameter tuning.

Ferroelectric tunneling junctions (FTJ) are considered to be the intrinsically most energy efficient memristors. In this work, specific electrical features of ferroelectric hafnium-zirconium oxide based FTJ devices are investigated. Moreover, the impact on the design of FTJ-based circuits for edge computing applications is discussed by means of two example circuits.