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Novel synaptic architecture for brain inspired computing

#artificialintelligence

The brain and all its magnificent capabilities is powered by less than 20 watts. Stop to think about that for a second. As I write this blog my laptop is using about 80 watts, yet at only a fourth of the power, our brain outperforms state-of-the-art supercomputers by several orders of magnitude when it comes to energy efficiency and volume. For this reason it shouldn't be surprising that scientists around the world are seeking inspiration from the human brain as a promising avenue towards the development of next generation AI computing systems and while the IT industry has made significant progress in the past several years, particularly in using machine learning for computer vision and speech recognition, current technology is hitting a wall when it comes to deep neural networks matching the power efficiency of their biological counterpart, but this could be about to change. As reported last week in Nature Communications, my colleagues and I at IBM Research and collaborators at EPFL and the New Jersey Institute of Technology have developed and experimentally tested an artificial synapse architecture using 1 million devices--a significant step towards realizing large-scale and energy efficient neuromorphic computing technology.


Neuro-memristive Circuits for Edge Computing: A review

arXiv.org Artificial Intelligence

The volume, veracity, variability and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce the overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of memristive circuit and architectures in terms of edge computing perspective.


Synaptic Architecture for Brain Inspired Computing: IBM Research

#artificialintelligence

Our brain and all its magnificent capabilities is powered by less than 20 watts. Stop to think about that for a second. As I write this blog my laptop is using about 80 watts, yet at only a fourth of the power, our brain outperforms state-of-the-art supercomputers by several orders of magnitude when it comes to energy efficiency and volume. For this reason it shouldn't be surprising that scientists around the world are seeking inspiration from the human brain as a promising avenue towards the development of next generation AI computing systems and while the IT industry has made significant progress in the past several years, particularly in using machine learning for computer vision and speech recognition, current technology is hitting a wall when it comes to deep neural networks matching the power efficiency of their biological counterpart, but this could be about to change. As reported last week in Nature Communications, my colleagues and I at IBM Research and collaborators at EPFL and the New Jersey Institute of Technology have developed and experimentally tested an artificial synapse architecture using 1 million devices -- a significant step towards realizing large-scale and energy efficient neuromorphic computing technology.


IBM Creates Artificial Neurons from Phase Change Memory for Cognitive Computing

#artificialintelligence

A team of scientists at IBM Research in Zurich, have created an artificial version of neurons using phase-change materials to store and process data. These phase change based artificial neurons can be used to detect patterns and discover correlations in the areas of big data and unsupervised machine learning. The results of the decade-long research to use phase-change materials for memory applications were recently published in the journal Nature Nanotechnology. The research team is led by Evangelos Eleftheriou. The development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing is getting a lot of attention.


Memristor-based Synaptic Sampling Machines

arXiv.org Artificial Intelligence

Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.