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Showing 1–6 of 6 results
Advanced filters: Author: Irem Boybat Clear advanced filters

  • Memristive technology is a promising avenue towards realizing efficient non-von Neumann neuromorphic hardware. Boybat et al. proposes a multi-memristive synaptic architecture with a counter-based global arbitration scheme to address challenges associated with the non-ideal memristive device behavior.

    • Irem Boybat
    • Manuel Le Gallo
    • Evangelos Eleftheriou
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-12

  • Analogue in-memory computing (AIMC), with digital processing, forms a useful architecture for performant end-to-end execution of deep neural network models, but requires the development of sophisticated software stacks. This Perspective outlines the challenges in designing deep learning software stacks for AIMC-based accelerators, and suggests directions for future research.

    • Corey Lammie
    • Hadjer Benmeziane
    • Abu Sebastian
    Reviews
    Nature Reviews Electrical Engineering
    Volume: 2, P: 621-633

  • Analogue-memory-based neural-network training using non-volatile-memory hardware augmented by circuit simulations achieves the same accuracy as software-based training but with much improved energy efficiency and speed.

    • Stefano Ambrogio
    • Pritish Narayanan
    • Geoffrey W. Burr
    Research
    Nature
    Volume: 558, P: 60-67

  • Designing deep learning inference hardware based on in-memory computing remains a challenge. Here, the authors propose a strategy to train ResNet-type convolutional neural networks which results in reduced accuracy loss when transferring weights to in-memory computing hardware based on phase-change memory.

    • Vinay Joshi
    • Manuel Le Gallo
    • Evangelos Eleftheriou
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-13