Please use this identifier to cite or link to this item: http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4694
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dc.contributor.authorVohra, S K-
dc.contributor.authorThomas, S A.-
dc.contributor.authorShivdeep-
dc.contributor.authorSakare, M-
dc.contributor.authorDas, D M-
dc.date.accessioned2024-07-12T12:38:04Z-
dc.date.available2024-07-12T12:38:04Z-
dc.date.issued2024-07-12-
dc.identifier.urihttp://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4694-
dc.description.abstractAbstract: Spiking neural networks (SNNs) implemented in neuromorphic computing architectures promise a high degree of bio-plausibility and energy efficiency compared to the artificial neural network (ANN). Thus, SNN-based spiking associative memories are preferred for high capacity, area, and energy-efficient neural associative memories (NAMs). While most previously published works focused on ANN-based NAM, this work implements the full CMOS circuit of memristor crossbar-based spiking NAM for the first time. Instead of using any software-based memristive SPICE model or memristive devices that are yet not available in standard CMOS technology process design kits (PDKs), in our work, the CMOS-based memristive synapse circuit is employed to address practical circuit implementation challenges. The complete ON-chip learning of the system is demonstrated using the bio-plausible spike-timing-dependent plasticity (STDP) learning mechanism without employing any external coprocessor, e.g., microprocessor, field-programmable gate array (FPGA). The entire system is implemented at the transistor level using 180-nm standard CMOS technology to demonstrate the pattern recognition application. The robustness of the proposed circuit is also evaluated to demonstrate the tolerance against the CMOS fabrication non-idealities.en_US
dc.language.isoen_USen_US
dc.subjectIn situ learningen_US
dc.subjectmemristor crossbaren_US
dc.subjectspiketiming-dependent plasticity (STDP)en_US
dc.subjectspiking neural associative memory (NAM)en_US
dc.subjectspiking neural network (SNN)en_US
dc.titleFull CMOS Circuit for Brain-Inspired Associative Memory With On-Chip Trainable Memristive STDP Synapseen_US
dc.typeArticleen_US
Appears in Collections:Year-2023

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