Functional Interface Blocks for Neuromorphic Hardware: A Junction-Centered Framework
2026-06-02 • Emerging Technologies
Emerging Technologies
AI summaryⓘ
The authors address the challenge of connecting different types of electronic devices in neuromorphic hardware, which is inspired by the brain. They focus on how the connections, or junctions, work rather than just the devices themselves, proposing a method that assigns specific roles to each side of a connection. They demonstrate this with a hardware setup that simulates learning using special devices called memristors and unijunction transistors. Their approach helps designers create and analyze complex systems where many different device types must work together smoothly.
Neuromorphic hardwareMemristorUnijunction transistorCurrent conveyor (CCII)Load-line conditionsJunction interfacePavlovian conditioningSynapseDrive/sense rolesFunctional interface blocks
Authors
Wellington Avelino, Yann Beillard, Fabien Allibart, Dominique Drouin, Gilberto Medeiros-Ribeiro
Abstract
Heterogeneous neuromorphic hardware integrates devices with dissimilar electrical characteristics and dynamics, making functional compatibility at their interconnections a primary design challenge. Direct coupling alone is insufficient to ensure correct operation, because the load-line conditions established at each junction determine the effective operating regime. Here, we propose a junction-centered interface framework in which inter-device connections are described through assigned drive/sense roles and organized into canonical functional interface blocks. As a concrete hardware realization, a second-generation current conveyor (CCII)-based implementation is then adopted as a composite realization of these interface primitives. The framework is validated experimentally in a Pavlovian-conditioning demonstrator combining a memristive synapse with a unijunction-transistor (UJT) post-neuron. By linking local junction conditions to reusable interface functions, the proposed methodology provides a systematic basis for the design and analysis of heterogeneous neuromorphic systems.