2019, Milo, V., Zambelli, C., Olivo, P.

Training and recognition with neural networks generally require high throughput, high energy efficiency, and scalable circuits to enable artificial intelligence tasks to be operated at the edge, i.e., in battery-powered portable devices and other limited-energy environments. In this scenario, scalable resistive memories have been proposed as artificial synapses thanks to their scalability, reconfigurability, and high-energy efficiency, and thanks to the ability to perform analog computation by physical laws in hardware. In this work, we study the material, device, and architecture aspects of resistive switching memory (RRAM) devices for implementing a 2-layer neural network for pattern recognition. First, various RRAM processes are screened in view of the device window, analog storage, and reliability. Then, synaptic weights are stored with 5-level precision in a 4 kbit array of RRAM devices to classify the Modified National Institute of Standards and Technology (MNIST) dataset. Finally, classification performance of a 2-layer neural network is tested before and after an annealing experiment by using experimental values of conductance stored into the array, and a simulation-based analysis of inference accuracy for arrays of increasing size is presented. Our work supports material-based development of RRAM synapses for novel neural networks with high accuracy and low-power consumption. © 2019 Author(s).

2021, Du, Nan, Schmidt, Heidemarie, Polian, Ilia

Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and in-memory computing (IMC), but there is a rising interest in using memristive technologies for security applications in the era of internet of things (IoT). In this review article, for achieving secure hardware systems in IoT, low-power design techniques based on emerging memristive technology for hardware security primitives/systems are presented. By reviewing the state-of-the-art in three highlighted memristive application areas, i.e. memristive non-volatile memory, memristive reconfigurable logic computing and memristive artificial intelligent computing, their application-level impacts on the novel implementations of secret key generation, crypto functions and machine learning attacks are explored, respectively. For the low-power security applications in IoT, it is essential to understand how to best realize cryptographic circuitry using memristive circuitries, and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security. This review article aims to help researchers to explore security solutions, to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.

2019, Becker, C., Karnaushenko, D., Kang, T., Karnaushenko, D.D., Faghih, M., Mirhajivarzaneh, A., Schmidt, O.G.

Novel robotic, bioelectronic, and diagnostic systems require a variety of compact and high-performance sensors. Among them, compact three-dimensional (3D) vector angular encoders are required to determine spatial position and orientation in a 3D environment. However, fabrication of 3D vector sensors is a challenging task associated with time-consuming and expensive, sequential processing needed for the orientation of individual sensor elements in 3D space. In this work, we demonstrate the potential of 3D self-assembly to simultaneously reorient numerous giant magnetoresistive (GMR) spin valve sensors for smart fabrication of 3D magnetic angular encoders. During the self-assembly process, the GMR sensors are brought into their desired orthogonal positions within the three Cartesian planes in a simultaneous process that yields monolithic high-performance devices. We fabricated vector angular encoders with equivalent angular accuracy in all directions of 0.14°, as well as low noise and low power consumption during high-speed operation at frequencies up to 1 kHz.