Toward Efficient System-on-Module for Design-Space Exploration of Analog Spiking Neural Networks - El-Masry, Moamen; Werner, Thilo; Zjajo, Amir; Weigel, Robert (IEEE Transactions on Circuits and Systems I-Regular Papers, 2024)
In this paper, we present an integrated system-on-module for design-space exploration of neurosynaptic behavior in non-volatile memory enhanced spiking neural networks. The system operates in locally-analog, globally-digital modus, facilitating the exploration and validation of both, individual computational components, and the characteristic spike-based features of neurosynaptic arrays. The key advantage of the system lies in its reconfigurable, adaptable, and interchangeable components, which enable precise and reproducible firing patterns. By leveraging these capabilities, various aspects of neurosynaptic behavior can be examined and manipulated. To enhance the weight retention mechanism, the platform incorporates embedded resistive-RAM, ensuring the preservation of synaptic weights. This integration further supports the accurate representation and processing of synaptic information. Experimental results in 28 nm CMOS technology demonstrate the feasibility and effectiveness of the proposed methodology in characterizing spiking neural network components.
CRIS-Team Scopus Importer:2024-06-14
Keywords: Artificial intelligence; Brain-inspired computing; near-memory computing; neuromorphic VLSI design; Neurons; non-volatile memory; Nonvolatile memory; Random access memory; Reliability; Resistance; resistive-RAM; spiking neural networks; Synapses; system-on-module