Top Research Papers on Neuromorphic Computing

To understand the theoretical underpinnings of the neuromorphic approach and predict the likelihood of its implementation within the next decade, it is vital to understand the rise of high performance and Quantum computing as promising alternatives.

An introduction to neuromorphic computing, why this and other novel new computing systems are needed, and what technologies currently exist in the neuro-morphic field are provided.

From the ancient Greeks comparing memory to a 'seal ring in wax,' to the 19th century brain as a 'telegraph switching circuit', to Freud's subconscious desires 'boiling over like a steam engine,' to a hologram, and finally, the computer.

Mainly the neuromorphic computing focus on matching a human brain flexibility, efficency and ability to learn and grab the things from physical environment with the energy efficiency of human brain.

Progress was made on both the hardware and software side of neuromorphic computing research setting the stage for future agile information systems.

A source of single photons that meets three important criteria for use in quantum-information systems has been unveiled in China by an international team of physicists.

The conventional Von Neumann architecture is explored and its shortcomings are outlined; neuromorphic architecture as an alternative and its evolution are described; and the key challenges hindering neuromorphic computing development are addressed.

A new design rule for developing a brain inspired computing system based on some recent findings in brain science is proposed and a neuromorphic chip, named `Tianji' chip is designed and fabricated.

This work introduces SONIC, a software-defined hardware design methodology to make neuromorphic computing accessible to the general computing community, and evaluates SONIC using three spiking datasets.

The intersection of neuromorphic computing and BMI has great potential to lead the development of reliable, low-power implantable BMI devices and advance the development and application of BMI.

This work identifies several science areas where neuromorphic computing can either make an immediate impact or the societal impact would be extremely high if the technological barriers can be addressed.

This paper will explore how technology options affect design choices, using both digital and analog circuit designs suitable for neural nets.

By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making.

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This perspective article discusses the different implementations of quantum neuromorphic networks with digital and analog circuits, highlight their respective advantages, and review exciting recent experimental results.

This review highlights recent advancements, ongoing research efforts, and potential future directions, illustrating how neuromorphic computing can redefine the landscape of AI by enabling systems that are not only faster and more efficient but also capable of real-time learning and decision-making in dynamic environments.

The conclusion is that the neuromorphic computers will replace the conventional Von Neumann computers, boosting the further development in computing power, breaking its limit.

This paper presents the history, the need for Neuromorphic computing, the functionalities, the current projects, their main features and technical capabilities of Neuromorph computing.

The origin of neuromorphic computing can be traced back to 1949, when McCulloch and Pitts proposed a mathematical model of the biological neuron and Rosenblatt developed the model of a fundamental neural network called multiple-layer perceptron (MLP), which constitutes the backbone for the emerging concept of deep neural networks (DNNs).

If deep learning wants to follow up the victory that deep learning has won and further build a general, efficient and brain-like intelligence, it is suggested to develop a paradigm of neuromorphic computing, which combines architecture, algorithms, circuits and devices tightly.