AI Insight
Researchers developed a new metric called local information processing capacity (L-IPC) that maps how individual components within neuromorphic computing systems perform computations, rather than just measuring overall system performance. By analyzing three different neuromorphic systems with similar global capabilities, they discovered that computational activity is distributed very differently across each system's components, ranging from uniform to highly structured patterns. This local mapping enabled targeted removal of less-important components while maintaining performance better than standard pruning methods.
Why it matters
This framework could significantly improve the design and efficiency of neuromorphic computing systems by identifying which physical components are actually contributing to computation. The ability to prune systems while preserving performance has practical implications for creating smaller, more energy-efficient brain-inspired computers and optimizing them for specific tasks.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: Many neuromorphic systems possess rich internal computational structures shaped by their physical dynamics. However, existing metrics characterize only global computation, leaving these local structures unresolved despite their importance for system interpretation, optimization and miniaturization. Here, we introduce local information processing capacity (L-IPC), which represents individual dynamical states as vectors in an infinite-dimensional Hilbert space and decomposes their computational properties through subspace projections onto nonlinearity orders and memory steps, with the residual quantifying computational irrelevance. Applied to three systems with comparable global capacities (an echo state network, an ion-gating reservoir and a spin-wave reservoir), L-IPC reveals markedly different distributions of computation, ranging from homogeneous to spatially, temporally and spectrally structured patterns. These local profiles further guide task-specific pruning, outperforming standard approaches at the same size in the structured systems. These results establish L-IPC as a general framework for anatomizing computation and guiding task-adaptive design in neuromorphic systems.
Source: Local information processing capacity: anatomy of computation in neuromorphic systems