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The co-location of memory and processing is a core principle of neuromorphic computing. A local memory device for synaptic weight storage has long been recognized as an enabling element for large-scale, high-performance neuromorphic…
We present a design-scheme for ultra-low power neuromorphic hardware using emerging spin-devices. We propose device models for 'neuron', based on lateral spin valves and domain wall magnets that can operate at ultra-low terminal voltage of…
Establishing a reliable communication interface between the brain and electronic devices is of paramount importance for exploiting the full potential of neural prostheses. Current microelectrode technologies for recording electrical…
High-speed machine vision is increasing its importance in both scientific and technological applications. Neuro-inspired photonic computing is a promising approach to speed-up machine vision processing with ultralow latency. However, the…
Intense efforts have been made in recent years to realize nonlinear optical interactions at the single-photon level. Much of this work has focused on achieving strong third-order nonlinearities, such as by using single atoms or other…
Conventional semiconductor-based integrated circuits are gradually approaching fundamental scaling limits. Many prospective solutions have recently emerged to supplement or replace both the technology on which basic devices are built and…
Computational hardware designed to mimic biological neural networks holds the promise to resolve the drastically growing global energy demand of artificial intelligence. A wide variety of hardware concepts have been proposed, and among…
The demands of modern electronic components require advanced computing platforms for efficient information processing to realize in-memory operations with a high density of data storage capabilities towards developing alternatives to von…
All-electronic interrogation of biofluid flow velocity by sensors incorporated in ultra-low-power or self-sustained systems offers the promise of enabling multifarious emerging research and applications. Electrical sensors based on…
Cryogenic neuromorphic systems, inspired by the brains unparalleled efficiency, present a promising paradigm for next generation computing architectures.This work introduces a fully integrated neuromorphic framework that combines…
Advanced electro-optic processing combines electrical control with optical modulation and detection. For quantum photonic applications these processes must be carried out at the single photon level with high efficiency and low noise.…
Sufficient light-matter interactions are important for waveguide-coupled graphene optoelectronic devices. Using a microfiber-based lab-on-a-rod technique, we present a platform for ultra-long light-graphene interaction and design…
Bio-inspired neuromorphic cameras asynchronously record pixel brightness changes and generate sparse event streams. They can capture dynamic scenes with little motion blur and more details in extreme illumination conditions. Due to the…
Neuromorphic Computing implemented in photonic hardware is one of the most promising routes towards achieving machine learning processing at the picosecond scale, with minimum power consumption. In this work, we present a new concept for…
The continuous effort in making artificial neural networks more alike to human brain calls for the hardware elements to implement biological synapse-like functionalities. The recent experimental demonstration of ferroelectric-like FETs…
Neuromorphic computing seeks to replicate the spiking dynamics of biological neurons for brain-inspired computation. While electronic implementations of artificial spiking neurons have dominated to date, photonic approaches are attracting…
Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report…
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to digital systems. Though many devices have emerged as candidates for…
Neuromorphic computing architectures enable the dense co-location of memory and processing elements within a single circuit. This co-location removes the communication bottleneck of transferring data between separate memory and computing…
Deeplearning algorithms are revolutionising many aspects of modern life. Typically, they are implemented in CMOS-based hardware with severely limited memory access times and inefficient data-routing. All-optical neural networks without any…