Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

SpinLink: An interconnection system for the SpiNNaker biologically inspired multi-computer

SpiNNaker is a large-scale biologically-inspired multi-computer designed to model very heavily distributed problems, with the flagship application being the simulation of large neural networks. The project goal is to have one million processors included in a single machine, which consequently span many thousands of circuit boards. A computer of this scale imposes large communication requirements between these boards, and requires an extensible method of connecting to external equipment such as sensors, actuators and visualisation systems. This paper describes two systems that can address each of these problems.Firstly, SpinLink is a proposed method of connecting the SpiNNaker boards by using time-division multiplexing (TDM) to allow eight SpiNNaker links to run at maximum bandwidth between two boards. SpinLink will be deployed on Spartan-6 FPGAs and uses a locally generated clock that can be paused while the asynchronous links from SpiNNaker are sending data, thus ensuring a fast and glitch-free response. Secondly, SpiNNterceptor is a separate system, currently in the early stages of design, that will build upon SpinLink to address the important external I/O issues faced by SpiNNaker. Specifically, spare resources in the FPGAs will be used to implement the debugging and I/O interfacing features of SpiNNterceptor

Brain-inspired computing needs a master plan

New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-increasing rate. To realize this promise requires a brave and coordinated plan to bring together disparate research communities and to provide them with the funding, focus and support needed. We have done this in the past with digital technologies; we are in the process of doing it with quantum technologies; can we now do it for brain-inspired computing