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Analog compute‐in‐memory (CIM) systems are promising candidates for deep neural network (DNN) inference acceleration. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. Herein, a potential security vulnerability is identified wherein an adversary can reconstruct the user's private input data from a power side‐channel attack even without knowledge of the stored DNN model. An attack approach using a generative adversarial network is developed to achieve high‐quality data reconstruction from power leakage measurements. The analyses show that the attack methodology is effective in reconstructing user input data from power leakage of the analog CIM accelerator, even at large noise levels and after countermeasures. To demonstrate the efficacy of the proposed approach, an example of CIM inference of U‐Net for brain tumor detection is attacked, and the original magnetic resonance imaging medical images can be successfully reconstructed even at a noise level of 20% standard deviation of the maximum power signal value. This study highlights a potential security vulnerability in emerging analog CIM accelerators and raises awareness of needed safety features to protect user privacy in such systems.

Memristive devices have demonstrated rich switching behaviors that closely resemble synaptic functions and provide a building block to construct efficient neuromorphic systems. It is demonstrated that resistive switching effects are controlled not only by the external field, but also by the dynamics of various internal state variables that facilitate the ionic processes. The internal temperature, for example, works as a second‐state variable to regulate the ion motion and provides the internal timing mechanism for the native implementation of timing‐ and rate‐based learning rules such as spike timing dependent plasticity (STDP). In this work, it is shown that the 2nd state‐variable in a Ta₂O₅‐based memristor, its internal temperature, can be systematically engineered by adjusting the material properties and device structure, leading to tunable STDP characteristics with different time constants. When combined with an artificial post‐synaptic neuron, the 2nd‐order memristor synapses can spontaneously capture the temporal correlation in the input streaming events.

Memristors have emerged as transformative devices to enable neuromorphic and in‐memory computing, where success requires the identification and development of materials that can overcome challenges in retention and device variability. Here, high‐entropy oxide composed of Zr, Hf, Nb, Ta, Mo, and W oxides is first demonstrated as a switching material for valence change memory. This multielement oxide material provides uniform distribution and higher concentration of oxygen vacancies, limiting the stochastic behavior in resistive switching. (Zr, Hf, Nb, Ta, Mo, W) high‐entropy‐oxide‐based memristors manifest the “cocktail effect,” exhibiting comparable retention with HfO₂‐ or Ta₂O₅‐based memristors while also demonstrating the gradual conductance modulation observed in WO₃‐based memristors. The electrical characterization of these high‐entropy‐oxide‐based memristors demonstrates forming‐free operation, low device and cycle variability, gradual conductance modulation, 6‐bit operation, and long retention which are promising for neuromorphic applications.