Parallel Computing for Brain Simulation

Pastur-Romay, Lucas Antón; Porto-Pazos, Ana B.; Cedrón, Francisco

doi:10.2174/1568026617666161104105725

Cited by 8 publications

(6 citation statements)

References 191 publications

(203 reference statements)

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“…The neuromorphic architecture is a computing architecture which can integrate synapse and neurons in a compact manner, configure the topology of neural networks easily, and execute neuronal and synaptic computation efficiently. Unlike the von Neumann architecture, the neuromorphic architecture should integrate computing and memory and provide high‐density and parallel data storage and computation . Among the various parallel architectures for NCS, the crossbar architecture shows tremendous potential .…”

Section: Neuromorphic Memristor Crossbarmentioning

confidence: 99%

Neuromorphic Computing with Memristor Crossbar

Zhang

Huang

et al. 2018

Physica Status Solidi (a)

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Neural networks, one of the key artificial intelligence technologies today, have the computational power and learning ability similar to the brain. However, implementation of neural networks based on the CMOS von Neumann computing systems suffers from the communication bottleneck restricted by the bus bandwidth and memory wall resulting from CMOS downscaling. Consequently, applications based on large-scale neural networks are energy/ area hungry and neuromorphic computing systems are proposed for efficient implementation of neural networks. Neuromorphic computing system consists of the synaptic device, neuronal circuit, and neuromorphic architecture. With the two-terminal nonvolatile nanoscale memristor as the synaptic device and crossbar as parallel architecture, memristor crossbars are proposed as a promising candidate for neuromorphic computing. Herein, neuromorphic computing systems with memristor crossbars are reviewed. The feasibility and applicability of memristor crossbars based neuromorphic computing for the implementation of artificial neural networks and spiking neural networks are discussed and the prospects and challenges are also described.

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Section: Neuromorphic Memristor Crossbarmentioning

confidence: 99%

Neuromorphic Computing with Memristor Crossbar

Zhang

Huang

et al. 2018

Physica Status Solidi (a)

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“…This review will focus only on digital neuromorphic chips, the IBM TrueNorth and the SpiNNaker chip, because are the most advanced projects, obtained the best results implementing DNNs and published the highest number of technical papers. For further details about other projects and the differences between digital, analog and hybrid neuromorphic chips, the reader should refer to other reviews [82,83]. …”

Section: Neuromorphic Chipsmentioning

confidence: 99%

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

Pastur-Romay

Cedrón

Pazos

et al. 2016

IJMS

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Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

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“…T HE human brain is characterised by its tolerance to faults/noise, concurrent processing capabilities, flexibility and high level of parallelisation when processing data. Furthermore, the adult human brain has a power consumption of about 400 Kcal per day, equivalent to 25 Watts [1]. Again, the human brain can reach 10-50 petaflops outperforming any Commercial-of-the-shelf (COTS) CPU [2].…”

Section: Introductionmentioning

confidence: 99%

“…Again, the human brain can reach 10-50 petaflops outperforming any Commercial-of-the-shelf (COTS) CPU [2]. Despite CPUs outperforming the human brain when processing and transmitting sequential signals in several orders of magnitude, the human brain exceeds CPUs processing millions of signals in parallel using its massively parallel circuits [1], [2].…”

Section: Introductionmentioning

confidence: 99%