New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain.
Artificial neural networks (ANNs) exhibit learning abilities and can perform tasks which are difficult for conventional computing systems, such as pattern recognition, on-line learning and classification. Practical ANN implementations are currently hampered by the lack of efficient hardware synapses; a key component that every ANN requires in large numbers.
In the study, published in Nature Communications, the Southampton research team experimentally demonstrated an ANN that used memristor synapses supporting sophisticated learning rules in order to carry out reversible learning of noisy input data.
Memristors are electrical components that limit or regulate the flow of electrical current in a circuit and can remember the amount of charge that was flowing through it and retain the data, even when the power is turned off.
Lead author Dr Alex Serb, from Electronics and Computer Science at the University of Southampton, said: “If we want to build artificial systems that can mimic the brain in function and power we need to use hundreds of billions, perhaps even trillions of artificial synapses, many of which must be able to implement learning rules of varying degrees of complexity. Whilst currently available electronic components can certainly be pieced together to create such synapses, the required power and area efficiency benchmarks will be extremely difficult to meet -if even possible at all- without designing new and bespoke ‘synapse components’.
“Memristors offer a possible route towards that end by supporting many fundamental features of learning synapses (memory storage, on-line learning, computationally powerful learning rule implementation, two-terminal structure) in extremely compact volumes and at exceptionally low energy costs. If artificial brains are ever going to become reality, therefore, memristive synapses have to succeed.”
Acting like synapses in the brain, the metal-oxide memristor array was capable of learning and re-learning input patterns in an unsupervised manner within a probabilistic winner-take-all (WTA) network. This is extremely useful for enabling low-power embedded processors (needed for the Internet of Things) that can process in real-time big data without any prior knowledge of the data.
Co-author Dr Themis Prodromakis, Reader in Nanoelectronics and EPSRC Fellow in Electronics and Computer Science at the University of Southampton, said: “The uptake of any new technology is typically hampered by the lack of practical demonstrators that showcase the technology’s benefits in practical applications. Our work establishes such a technological paradigm shift, proving that nanoscale memristors can indeed be used to formulate in-silico neural circuits for processing big-data in real-time; a key challenge of modern society.
“We have shown that such hardware platforms can independently adapt to its environment without any human intervention and are very resilient in processing even noisy data in real-time reliably. This new type of hardware could find a diverse range of applications in pervasive sensing technologies to fuel real-time monitoring in harsh or inaccessible environments; a highly desirable capability for enabling the Internet of Things vision.”
A research group of the UPV/EHU-University of the Basque Country has made progress in obtaining bio-oils and raw materials from biomass using its patented reactor
The UPV/EHU’s Catalytic Processes for Waste Valorisation research group is working on various lines of research relating to renewable energies, one of which corresponds to the obtaining of bio-oils or synthetic petroleum using biomass. In a paper recently published in the scientific journal Fuel, the researchers have proposed using artificial neural networks to determine the heating power of each type of biomass using its composition as it is a highly irregular material.
Biomass is one of the main sources of energy and heat in the field of renewable energy production: it is any type of non-fossil organic matter, such as living plants, timber, agricultural and livestock waste, wastewater, solid urban organic waste, etc. The three most developed technologies for obtaining energy from biomass are as follows: pyrolysis (decomposition by heating in the absence of oxygen), gasification (reaction with air, oxygen or a blend of both and conversion into gas) and combustion (decomposition through heating with oxygen). The effectiveness and emission levels of these three processes change depending on the composition of the biomass as well as its properties, the experimental conditions and equipment used.
In collaboration with researchers at the University of Sao Carlos in Brazil and within the framework of a European project, members of the UPV/EHU’s Catalytic Processes for Waste Valorisation research group analysed the process to set up a refinery to obtain bio-oils or synthetic petroleum using biomass. Since “afterwards, using the bio-oil produced it is possible to obtain the same products that are obtained from petroleum; hydrogen as well as any other compound,” explained Martin Olazar, project leader and professor of the Department of Chemical Engineering. The reactor developed and patented by this research group, the conical spouted bed reactor, is highly suited to this process because it is suitable for handling irregular, sticky materials —biomass is a highly irregular material and difficult to handle using conventional technologies—.
Artificial neural networks to determine gross calorific value
In the design of the process to obtain bio-oils using biomass, certain variables need to be determined: the temperature that needs to be achieved, how this temperature is to be achieved, how much fuel (in this case how much biomass) needs to be burnt, etc. The gross calorific value is a key parameter in determining all these data: it is the heat (energy) that is released when a certain quantity of fuel is completely burnt. This parameter is essential in the analysis, design and improvement in biomass pyrolysis, gasification and combustion systems. The correlations existing in the literature give highly variable results depending on each type of biomass and its properties. So the researchers in the group are proposing that artificial neural networks be used to calculate this; they have proven empirically that the system gives very good results and they have reported on them in a paper recently published in the scientific journal Fuel.