Neural dynamical systems on ferroelectric compute-in-memory for real-time forecasting

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Abstract
Neural dynamical systems (NDSs) are expressive temporal predictors that capture continuous-time dynamics through fine-grained state updates. However, this sequential structure maps poorly onto digital hardware optimized for dense matrix operations, a mismatch that analog neuromorphic computing, with its native continuous-time dynamics, can resolve. We introduce FerroNDS, a neuromorphic system built from two analog primitives: an integrator for temporal accumulation and an oscillator for frequency-selective filtering. We map this system onto compute-in-memory (CIM) hardware based on multi-bit ferrodiodes. A 48-unit FerroNDS bank computes a short-time Fourier transform whose spectrogram correlates at 0.93 with a Hann reference, while a 32-unit instance forecasts frequency and fault power at horizons from 14.5 to 228.5 ms and chaotic dynamics over a rollout of 10 Lyapunov times. Both results use behavioral models of the analog primitives, fitted and validated against LTspice traces of the full circuit. The system achieves sub-watt, real-time operation with per-neuron per-inference energy of 82.9–89.5 nJ (200 Hz) and 14.7–14.8 nJ (10 kHz), with per-layer latency of 3.18 ms (200 Hz) and 63.66 μ s (10 kHz). To our knowledge, this is the first end-to-end integration of a ferrodiode into a neuromorphic computational framework, establishing ferroelectric CIM as a practical substrate for analog NDSs.
Funding Information
  • National Science Foundation (NSF) Graduate Research Fellowship Program (2022338725)

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