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DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

Author

Listed:
  • Simone D’Agostino

    (University of Zurich and ETH Zurich
    Université Grenoble Alpes)

  • Filippo Moro

    (University of Zurich and ETH Zurich
    Université Grenoble Alpes)

  • Tristan Torchet

    (University of Zurich and ETH Zurich)

  • Yiğit Demirağ

    (University of Zurich and ETH Zurich)

  • Laurent Grenouillet

    (Université Grenoble Alpes)

  • Niccolò Castellani

    (Université Grenoble Alpes)

  • Giacomo Indiveri

    (University of Zurich and ETH Zurich)

  • Elisa Vianello

    (Université Grenoble Alpes)

  • Melika Payvand

    (University of Zurich and ETH Zurich)

Abstract

Neuroscience findings emphasize the role of dendritic branching in neocortical pyramidal neurons for non-linear computations and signal processing. Dendritic branches facilitate temporal feature detection via synaptic delays that enable coincidence detection (CD) mechanisms. Spiking neural networks highlight the significance of delays for spatio-temporal pattern recognition in feed-forward networks, eliminating the need for recurrent structures. Here, we introduce DenRAM, a novel analog electronic feed-forward spiking neural network with dendritic compartments. Utilizing 130 nm technology integrated with resistive RAM (RRAM), DenRAM incorporates both delays and synaptic weights. By configuring RRAMs to emulate bio-realistic delays and exploiting their heterogeneity, DenRAM mimics synaptic delays and efficiently performs CD for pattern recognition. Hardware-aware simulations on temporal benchmarks show DenRAM’s robustness against hardware noise, and its higher accuracy over recurrent networks. DenRAM advances temporal processing in neuromorphic computing, optimizes memory usage, and marks progress in low-power, real-time signal processing

Suggested Citation

  • Simone D’Agostino & Filippo Moro & Tristan Torchet & Yiğit Demirağ & Laurent Grenouillet & Niccolò Castellani & Giacomo Indiveri & Elisa Vianello & Melika Payvand, 2024. "DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47764-w
    DOI: 10.1038/s41467-024-47764-w
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    References listed on IDEAS

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