Publication date: 24th July 2026
Processing-in-memory alleviates the conventional von Neumann bottleneck, but peripheral conversion between analog and digital domains remains a major source of energy and area overhead. Here, we report a signal-domain-conversion-free physical computing platform based on a Spiking Hopfield Network (SHN) implemented with NbOx-based selector-only Mott memory (SOMM) cells. The SOMM cell monolithically couples non-volatile synaptic resistance with volatile neuronal dynamics within a single device, allowing analog memory states to be directly converted into multi-bit digital outputs through spike counting. This intrinsic transduction from resistance states to spike counts eliminates the need for energy-intensive peripheral conversion stages. By selectively configuring the stochastic and deterministic programming/readout dynamics of the SHN, we implement two representative physical computing primitives with contrasting hardware requirements on the same platform: a physical unclonable function (PUF) and a simulated annealer (SA). The PUF achieves a vast challenge-response pair space of 2N-1 in an N-cell array, with efficient concealability and reconfigurability, while the SA achieves a 105-fold reduction in energy consumption compared with state-of-the-art GPUs. Overall, we demonstrate that the SHN platform performs computing in a single signal domain without conversions, offering a promising route toward next-generation physical computing architecture.
This work was supported by the Federal Ministry of Education and Research (BMBF, Germany) through the project NEUROTEC with Grant No. 16ME0398K, and the National Research Foundation of Korea (NRF) (Grant numbers: RS-2023-00216619, RS-2023-NR077077, RS-2023-00216992, 2022M3I7A4085484, and RS-2025-02433006). G. K. was supported by Alexander von Humboldt Foundation through Humboldt Research Fellowship.
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