Top Research Papers on Neuromorphic Computing

Approaches for the development of future at-scale neuromorphic systems based on principles of biointelligence are described, along with potential applications of scalable neuromorphic architectures and the challenges that need to be overcome.

The need and the possibility to conceive a photonic memristor are discussed, a positive outlook on the challenges and opportunities for the ambitious goal of realising the next generation of full-optical neuromorphic hardware is offered.

Inspired by recent progress in optogenetics and visual sensing, light has been increasingly incorporated into synaptic devices, paves the way to optoelectronic synaptic devices with a series of advantages such as wide bandwidth, negligible resistance–capacitance delay and power loss, and global regulation of multiple synaptic devices.

This survey reviews computing models and hardware platforms of existing neuromorphic computing systems, and introduces neuron and synapse models, followed by the discussion on how they will affect hardware design.

There is still a wide variety of challenges that restrict the rapid growth of neuromorphic algorithmic and application development, and opportunities for future development of algorithms and applications on these systems are discussed.

An overview of various neural networks with a focus on building a memristor‐based spike neural network neuromorphic computing system is provided, and an outlook for brain‐like computing is proposed.

Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term Plasticity, transition from short‐to‐long‐term memory, and learning‐experience behavior, are successfully emulated.

This review of synaptic devices that mimic synaptic functions is discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals.

Recent advances in integrated photonic neuromorphic neuromorphic systems are reviewed, current and future challenges are discussed, and the advances in science and technology needed to meet those challenges are outlined.

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted c...

The recent advances of ONNs based on different approaches to photonic multiplexing are reviewed, and the outlook on key technologies needed to further advance these photonicMultiplexing/hybrid-multiplexing techniques of Onns are presented.

Arrays of memcapacitor devices that work via charge shielding can be used to implement artificial neural networks and could potentially offer an energy efficiency of 29,600 tera-operations per second per watt.

It is shown that lattices of exciton-polariton condensates accomplish neuromorphic computing with outstanding accuracy thanks to their high optical nonlinearity and it is demonstrated that this neural network significantly increases the recognition efficiency compared to the linear classification algorithms on one of the most widely used benchmarks, the MNIST problem.

Recent advances in WDM-based ONNs are reviewed, focusing on methods that use integrated microcombs to implement ONN's, and results for human image processing using an optical convolution accelerator operating at 11 Tera operations per second are presented.

The critical characteristic parameters of memristors affecting the performance of RC systems, such as reservoir size and decay time, are identified and discussed, and the challenges this field faces in reliable and accurate task processing are summarized and forecasted.

The progress, challenges, and opportunities for both volatile and nonvolatile memristor in the level of materials, integration technology, algorithm, and system are highlighted in this review.

The discovery of an alloyed memristor with alloyed conduction channels enables stable and controllable device operation with high switching uniformity and allows the fabrication of large-scale crossbar arrays that feature a high device yield and accurate analogue programming capability.

The electrical current-induced creation, motion, detection and deletion of skyrmions in ferrimagnetic multilayers can be used to mimic the behaviour of biological synapses, providing devices that could be used for neuromorphic computing tasks such as pattern recognition.

These insights uncover computing principles, primitives, and algorithms on different levels of abstraction and call for more research into the basis of neural computation and neuronally inspired computing hardware.

How novel material properties enable complex dynamics and define different orders of complexity in memristor devices and systems are discussed, which enable new computing architectures that offer dramatically greater computing efficiency than conventional computers.