Environmentally Sustainable AI: Spike-Based Machine Intelligence

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  • Date
    5 March 2026
    Timeframe
    16:00 - 17:00 CET Geneva
    Duration
    60 minutes

      AI is poised to revolutionize society, yet its escalating energy demands pose a formidable challenge to its long-term sustainability. This talk will present a bio-inspired and integrative vision for building intelligent systems that are not only powerful but also energy-efficient and inclusive. Drawing from their recent work in neuromorphic computing and algorithm-hardware co-design, Prof. Priya Panda will discuss how spiking neural networks (SNNs), a class of AI models inspired by the brain, can offer a compelling alternative. These models leverage temporal processing to improve robustness and dramatically reduce energy consumption, latency, and computational overhead. From a hardware perspective, Priya Panda examines how memory and sparsity management can accelerate SNNs on general-purpose platforms.

       

      Session Objectives:

      By the end of this session, participants will be able to:

      • Explain how the functioning of the human brain can inspire new neural network architectures.
      • Understand what a “spike” is and why “spiking neural networks” can be an energy-efficient alternative to traditional neural networks.
      • Describe whether and how hardware can be designed to implement spiking neural networks.

       

      Recommended Mastery Level / Prerequisites:

      Participants should have an intermediate understanding  of neural networks, training vs interference, common components, as well as an introductory-level knowledge of computing systems.

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