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Neuromorphic Architectures for Scientific Computing: a Structural Characterization Case Study
Conference proceeding

Neuromorphic Architectures for Scientific Computing: a Structural Characterization Case Study

M. L. Varshika, Jonathan Hollenbach, Nicolas Bohm Agostini, Ankur Limaye, Marco Minutoli, Vito Giovanni Castellana, Joseph Manzano, Anup Das, Mitra Taheri and Antonino Tumeo
Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design, pp 1-9
26 Oct 2025
DOI: https://doi.org/10.1109/ICCAD66269.2025.11240880

Abstract

Computer architecture Energy efficiency Hardware Hopfield Network Hopfield neural networks Image reconstruction Materials science and technology Neuromorphic Computing Neuromorphic engineering Noise reduction Real-time systems Recurrent Network Spiking Neural Network (SNN) System-on-chip Variation Autoencoder (VAE)
Neuromorphic computing offers a promising paradigm for energy-efficient edge processing in scientific applications, such as the real-time analysis of Electron Energy Loss Spectroscopy (EELS) data from Transmission Electron Microscopes (TEMs). Current methods, primarily based on Spiking Variational Autoencoders (S-VAE), are constrained by high computational overhead. To address this, we propose an energy-efficient Spiking Hopfield Network (S-Hopfield) for online encoding and decoding of structural dynamics. Our approach leverages the inherent associative memory of Hopfield networks to robustly denoise and reconstruct spectral images, outperforming an S-VAE model in both image quality metrics and hardware efficiency. Quantitatively, the S-Hopfield network achieved a Mean Squared Error (MSE) of 0.54, a 28% improvement over the S-VAE's MSE of 0.75. On a Xilinx Virtex-7 FPGA, the S-Hopfield's core inference engine consumed a mere 0.25 W, representing a 51% reduction in power compared to the S-VAE's 0.51 W. These results demonstrate that the S-Hopfield network provides a superior, low-power solution for real-time spectral analysis at the edge, paving the way for autonomous experimental control in material science.

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