TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration

TL;DR

TEXEL is a neuromorphic processor platform that integrates on-chip learning with emerging memory devices, bridging the gap between CMOS neuromorphic systems and novel device technologies for advanced brain-inspired computing.

Contribution

The paper introduces TEXEL, a mixed-signal neuromorphic architecture that facilitates the integration and testing of emerging devices with on-chip learning capabilities.

Findings

Successful chip measurements and simulations demonstrate TEXEL's readiness for device integration.

TEXEL enables testing of bio-inspired learning algorithms with novel devices.

Establishes a practical link between neuromorphic computation and emerging device research.

Abstract

Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with CMOS circuitry. To address this, we introduce TEXEL, a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. TEXEL serves as an accessible platform to bridge the gap between CMOS-based neuromorphic computation and the latest advancements in emerging devices. In this paper, we demonstrate the readiness of TEXEL for device integration through comprehensive chip measurements and simulations. TEXEL provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research.