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A hallmark of biological intelligence is neural reuse,the ability to preserve past learning and repurpose it for new tasks and changing environments. Photonic neural hardware offers high-bandwidth, low-latency computation, but current…
Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and…
Molecule-based devices are envisioned to complement silicon devices by providing new functions or already existing functions at a simpler process level and at a lower cost by virtue of their self-organization capabilities. Moreover, they…
The speed of silicon-based transistors has reached an impasse in the recent decade, primarily due to scaling techniques and the short-channel effect. Conversely, graphene (a revolutionary new material possessing an atomic thickness) has…
This project explores the use of non-volatile synapses in neuromorphic computing for pattern recognition tasks through a comprehensive simulation-based approach. The main approach is through spintronic synapses, which leverage the…
The potential of memristive devices is often seeing in implementing neuromorphic architectures for achieving brain-like computation. However, the designing procedures do not allow for extended manipulation of the material, unlike CMOS…
This paper provides an overview on graphene solution-gated field effect transistors (SGFETs) and their applications in bioelectronics. The fabrication and characterization of arrays of graphene SGFETs is presented and discussed with respect…
Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could…
A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog…
Highly efficient information processing in brain is based on processing and memory components called synapses, whose output is dependent on the history of the signals passed through them. Here we have developed an artificial synapse with…
Spintronics exhibits significant potential in neuromorphic computing system with high speed, high integration density, and low dissipation. In this letter, we propose an ultralow-dissipation spintronic memristor composed of a synthetic…
The development of artificial intelligence (AI) and robotics are both based on the tenet of "science and technology are people-oriented", and both need to achieve efficient communication with the human brain. Based on multi-disciplinary…
The memory demands of large-scale deep neural networks (DNNs) require synaptic weight values to be stored and updated in off-chip memory like dynamic random-access memory, which reduces energy efficiency and increases training time.…
Artificial graphene consisting of honeycomb lattices other than the atomic layer of carbon has been shown to exhibit electronic properties similar to real graphene. Here, we reverse the argument to show that transport properties of real…
Enhancing the spin-orbit interaction in graphene, via proximity effects with topological insulators, could create a novel 2D system that combines nontrivial spin textures with high electron mobility. In order to engineer practical…
Haptic feedback is the most significant sensory interface following visual cues. Developing thin, flexible surfaces that function as haptic interfaces is important for augmenting virtual reality, wearable devices, robotics and prostheses.…
Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial…
Orchestration of diverse synaptic plasticity mechanisms across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive neuromorphic systems, devices that are capable to emulate…
We demonstrate an electrolyte-gated hybrid nanoparticle/organic synapstor (synapse-transistor, termed EGOS) that exhibits short-term plasticity as biological synapses. The response of EGOS makes it suitable to be interfaced with neurons:…
We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and…