See the agenda here.

Abstract: How can “time” be harnessed to boost neural network performance? The brain is a marvel of computation and memory, processing vast amounts of sensory data with an efficiency that puts modern electronics to shame. Reducing the megawatts consumed by hyperscale datacenters to the mere 10 watts the brain requires demands a fundamental shift – leveraging time. We will explore how temporal dynamics enhance neural network efficiency and performance. We will explore the Matrix-Multiply-free language model, where information is distributed across sequences, requiring the model to “learn to forget” in order to utilize limited cache effectively. Ultimately, by embracing temporal strategies, we pave the way toward neuromorphic computing systems that are not only more efficient but also closer to the elegant and sustainable designs found in nature. This exploration marks a step forward in reducing energy demands while advancing the capabilities of artificial intelligence.

Jason Eshraghian and Steven Abreu will be co-presenting a half-day tutorial at IEEE ISCAS 2025 in London, UK.