The European Union is funding research into the design of future microchips. Glasgow University professor Asen Asenov says developers need new circuit and system design to shrink the size of transistors for more powerful circuits. Glasgow is participating in the Tera-scale Reliable Adaptive Memory Systems (TRAMS) consortium, along with Intel Iberia, Interuniversitair Micro-Elektronica Centrium, and the Universitat Politecnica de Catalunya. “We hope this project will result in new chip design paradigms for building reliable memory systems out of unreliable nanoscale components cheaply and effectively, heralding the era of terascale computing,” Asenov says. TRAMS will focus on next-generation complementary metal-oxide semiconductor transistors and microchips.

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Robots that serve and prepare food have been developed by various institutions and companies, mainly to promote new technological breakthroughs, to imbue robots with personalities to help overcome an animus toward them, and to position the machines for work in other industries. For instance, researchers at Switzerland’s Learning Algorithms and Systems Laboratory invented the Chief Cook Robot, an omelet-making machine that can be “taught” to perform complex tasks. Underlying such efforts is the desire to change the often threatening and distrustful way we perceive robots, says NASA Jet Propulsion Laboratory roboticist Heather Knight. The spate of food-serving and food-making robots calls attention to the fact that developers are migrating away from a previous focus on efficiency and toward one of personality and more comfortable human-machine interaction.

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A consortium of European research institutions is working on a new, photonic computing model under the aegis of the PHOCUS project. The system envisioned by the project uses light to communicate, potentially raising energy efficiency far above that of current supercomputers. The reservoir computer concept is inspired by the rapid information processing architecture of the human brain, in which stimuli or inputs are fed into neural networks or reservoirs. Inputs remain detectable in the reservoir for a certain time, and this input memory, coupled with the emerging response of the reservoir, converts the input into a large number of dynamical states of the reservoir, producing a high-dimensional state space. The researchers say that photonic systems could be employed to comprehend and eventually imitate some of the brain’s functionalities. PHOCUS’ ultimate target is photonic deployment of reservoir computing, running at high data rates, as an alternative to supercomputers for operations that require reduced size and less power consumption.

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