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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. [ABSTRACT FROM AUTHOR]

Neuromorphic systems using memristors as artificial synapses have attracted broad interest for energy-efficient computing applications. However, networks based on these purely passive devices can be affected by parasitic effects such as series resistance and sneak path problems. Here, we analyze the effects of parasitic factors on the performance of memristor-based neuromorphic systems. During vector-array multiplication, the line resistance can cause significant distortion of the output current and the activity of the corresponding neurons. An approach to compensate the line resistance effects based on an approximate model consisting of only few known parameters is proposed and shows excellent ability to capture the complex network behavior. During training and feature detection, the series resistance can cause significant degradation of the learned dictionary, with only a few dominant neurons being trained. Using a scaling factor based on the proposed simple model, these effects can be successfully mitigated, and the correct network operations can be restored. These results provide insight and practical measures on the parasitic effects for implementation of the neuromorphic system using memristor arrays. [ABSTRACT FROM PUBLISHER]

Memristors have attracted broad interest as a promising candidate for future memory and computing applications. Particularly, it is believed that memristors can effectively implement synaptic functions and enable efficient neuromorphic systems. Most previous studies, however, focus on implementing specific synaptic learning rules by carefully engineering external programming parameters instead of focusing on emulating the internal cause that leads to the apparent learning rules. Here, it is shown that by taking advantage of the different time scales of internal oxygen vacancy ( VO) dynamics in an oxide-based memristor, diverse synaptic functions at different time scales can be implemented naturally. Mathematically, the device can be effectively modeled as a second-order memristor with a simple set of equations including multiple state variables. Not only is this approach more biorealistic and easier to implement, by focusing on the fundamental driving mechanisms it allows the development of complete theoretical and experimental frameworks for biologically inspired computing systems. [ABSTRACT FROM AUTHOR]

Memristors have been extensively studied for data storage and low-power computation applications. In this study, we show that memristors offer more than simple resistance change. Specifically, the dynamic evolutions of internal state variables allow an oxide-based memristor to exhibit Ca2+-like dynamics that natively encode timing information and regulate synaptic weights. Such a device can be modeled as a second-order memristor and allow the implementation of critical synaptic functions realistically using simple spike forms based solely on spike activity. [ABSTRACT FROM AUTHOR]