A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing.

Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency.

Adaptive Extreme Edge Computing for Wearable Devices.

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing.

Stimulated Ionic Telegraph Noise in Filamentary Memristive Devices.

Random telegraph noise is a widely investigated phenomenon affecting the reliability of the reading operation of the class of memristive devices whose operation relies on formation and dissolution of conductive filaments. The trap and the release of electrons into and from defects surrounding the filament produce current fluctuations at low read voltages. In this work, telegraphic resistance variations are intentionally stimulated through pulse trains in HfO-based memristive devices.

Extended memory lifetime in spiking neural networks employing memristive synapses with nonlinear conductance dynamics.

Spiking neural networks (SNNs) employing memristive synapses are capable of life-long online learning. Because of their ability to process and classify large amounts of data in real-time using compact and low-power electronic systems, they promise a substantial technology breakthrough. However, the critical issue that memristor-based SNNs have to face is the fundamental limitation in their memory capacity due to finite resolution of the synaptic elements, which leads to the replacement of old memories with new ones and to a finite memory lifetime.

Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing.

The development of devices that can modulate their conductance under the application of electrical stimuli constitutes a fundamental step towards the realization of synaptic connectivity in neural networks. Optimization of synaptic functionality requires the understanding of the analogue conductance update under different programming conditions. Moreover, properties of physical devices such as bounded conductance values and state-dependent modulation should be considered as they affect storage capacity and performance of the network.

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.

Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.