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Showing 1–4 of 4 results
Advanced filters: Author: Emre O. Neftci Clear advanced filters

  • Neural sampling machines make use of noise to perform learning. Here, Dutta et al. present a hybrid stochastic synapse composed out of a ferroelectric transistor combined with a stochastic selector exhibiting multiplicative synaptic noise required for implementing a neural sample machine.

    • Sourav Dutta
    • Georgios Detorakis
    • Suman Datta
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-10

  • Research on reinforcement learning in artificial agents focuses on a single complex problem within a static environment. In biological agents, research focuses on simple learning problems embedded in flexible, dynamic environments. The authors review the literature on these topics and suggest areas of synergy between them.

    • Emre O. Neftci
    • Bruno B. Averbeck
    Reviews
    Nature Machine Intelligence
    Volume: 1, P: 133-143

  • Brain-inspired neuromorphic algorithms and systems have shown essential advance in efficiency and capabilities of AI applications. In this Perspective, the authors introduce NeuroBench, a benchmark framework for neuromorphic approaches, collaboratively designed by researchers across industry and academia.

    • Jason Yik
    • Korneel Van den Berghe
    • Vijay Janapa Reddi
    ReviewsOpen Access
    Nature Communications
    Volume: 16, P: 1-24

  • The Perspective explores the future design of lifelong learning artificial intelligence (AI) accelerators that are intended for deployment in untethered environments, identifying key desirable capabilities for such edge AI accelerators and guidance on metrics to evaluate them.

    • Dhireesha Kudithipudi
    • Anurag Daram
    • Benjamin Epstein
    Reviews
    Nature Electronics
    Volume: 6, P: 807-822