{"id":11146,"date":"2026-02-01T04:12:57","date_gmt":"2026-02-01T04:12:57","guid":{"rendered":"https:\/\/mcsoc-forum.org\/site\/?page_id=11146"},"modified":"2026-02-04T00:28:04","modified_gmt":"2026-02-04T00:28:04","slug":"neurocore-t9","status":"publish","type":"page","link":"https:\/\/mcsoc-forum.org\/site\/index.php\/neurocore-t9\/","title":{"rendered":"Brain-Inspired Algorithm\u2013Hardware Co-Optimization"},"content":{"rendered":"\n
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\n \n \u2190 Back to the NeuroCore 2026 Page\n <\/a>\n<\/p>\n

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Track Chair<\/h2>\n
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\"Dr.<\/figure>

Dr. Alberto Marchisio<\/a><\/strong>
New York University (NYU) Abu Dhabi, UAE<\/span><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

Call for Papers: Brain\u2011Inspired Algorithm\u2013Hardware Co\u2011Optimization<\/strong><\/h2>\n\n\n\n

Neuromorphic computing thrives when algorithms and hardware are conceived as a unified system rather than independent layers. This track focuses on cross\u2011disciplinary innovations that co\u2011optimize brain\u2011inspired algorithms with their underlying hardware substrates to achieve maximal efficiency, scalability, and real\u2011time adaptability.
As neuromorphic systems evolve toward event\u2011driven, low\u2011power, and cognitively capable architectures, co\u2011design becomes essential for aligning computational models with device\u2011level constraints, memory hierarchies, communication fabrics, and emerging technologies. This track invites contributions that advance the theory, design, and implementation of algorithm\u2013hardware co\u2011optimization across all layers of the neuromorphic stack\u2014from learning rules and neural models to circuits, architectures, and full systems.<\/p>\n\n\n\n

Topics of Interest include, but are not limited to:<\/strong><\/h3>\n\n\n\n
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  • Brain\u2011Inspired and Neuromorphic Algorithms<\/strong>\n
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    • Spiking neural networks (SNNs) and event\u2011driven learning<\/li>\n\n\n\n
    • Bio\u2011plausible learning rules and neural coding strategies<\/li>\n\n\n\n
    • Cognitive and adaptive algorithmic models<\/li>\n<\/ul>\n<\/li>\n\n\n\n
    • Algorithm\u2013Hardware Co\u2011Design<\/strong>\n
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      • Cross\u2011layer optimization from device to system level<\/li>\n\n\n\n
      • Mapping algorithms to neuromorphic substrates efficiently<\/li>\n\n\n\n
      • Co\u2011optimization frameworks for latency, energy, and accuracy<\/li>\n<\/ul>\n<\/li>\n\n\n\n
      • Event\u2011Driven and Low\u2011Power Architectures<\/strong>\n
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        • Event\u2011driven processing pipelines<\/li>\n\n\n\n
        • Ultra\u2011low\u2011power neuromorphic accelerators<\/li>\n\n\n\n
        • Real\u2011time adaptive and autonomous systems<\/li>\n<\/ul>\n<\/li>\n\n\n\n
        • In\u2011Memory and Near\u2011Memory Computing<\/strong>\n
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          • Memory\u2011centric architectures for neuromorphic workloads<\/li>\n\n\n\n
          • Compute\u2011in\u2011memory (CIM) and analog in\u2011memory processing<\/li>\n\n\n\n
          • Memory\u2013algorithm co\u2011design for SNNs and sparse workloads<\/li>\n<\/ul>\n<\/li>\n\n\n\n
          • Learning\u2011on\u2011Chip and On\u2011Device Intelligence<\/strong>\n
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            • Online learning, continual learning, and local adaptation<\/li>\n\n\n\n
            • Hardware\u2011efficient training and inference methods<\/li>\n\n\n\n
            • Lightweight models for edge and embedded neuromorphic systems<\/li>\n<\/ul>\n<\/li>\n\n\n\n
            • Emerging Devices and Novel Substrates<\/strong>\n
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              • RRAM, PCM, FeFETs, spintronics, photonics, and other emerging devices<\/li>\n\n\n\n
              • Device\u2011level characteristics and their algorithmic implications<\/li>\n\n\n\n
              • Hybrid CMOS\u2013non\u2011CMOS neuromorphic platforms<\/li>\n<\/ul>\n<\/li>\n\n\n\n
              • System\u2011Level Integration and Demonstrations<\/strong>\n
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                • End\u2011to\u2011end neuromorphic systems showcasing co\u2011optimized design<\/li>\n\n\n\n
                • Benchmarks, evaluation methodologies, and workload characterization<\/li>\n\n\n\n
                • Applications in robotics, sensing, autonomous systems, and cognitive computing<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n

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