Human brain inspires computer memory

How is it possible to create computer memory that is both faster and consumes less energy? Researchers at the Institut d'électronique fondamentale (CNRS/Université Paris-Sud) and CEA-List have unlocked the physical mechanisms involved in new-generation magnetic memory, and have shown that these mechanisms could be used as “synapses” in a new type of neuro-inspired system, able to learn how to store and retrieve information. Their work was published online in the journal IEEE Transactions on Biomedical Circuits
and Systems
on April 15, 2015.

Today there are two main categories of computer memory, without which our computers would not be able to store and retrieve information. Volatile memory can process a large amount of data in a short period of time, but it is dependent on continuous electrical power to retain the information it has recorded. On the contrary, non-volatile memory, such as USB keys or CDs, are not dependent on a source of electricity, but they are much slower. An alternative is being developed: magnetic memory (STT-MRAM), which combines processing speed and energy independence, and stores information as a magnetic orientation rather than as an electric charge.

One of the main problems of this new technology is its high energy cost. To program magnetic tunnel junctions (MTJ), the basic nanocomponents of magnetic memory, electrical voltage is applied to their terminals. If the switching time is not sufficient, the programming can be incorrect, with a degree of randomness that depends on the duration of the programming pulse. This is referred to as probabilistic programming.  
For more conventional memory uses, which on the contrary require programming that excludes randomness, MTJ switching time must be extended in order to ensure sufficiently reliable programming. However, this programming strategy results in substantial energy consumption.

The researchers showed that the probabilistic programming of MTJs can become an advantage. They were actually the first to use MTJs as “synapses” (connections) of a system whose functioning is inspired by the human brain, in that it consumes very little energy, while being able to process very large amounts of data. Probabilistic programming is thus a way for the system to learn a function after a number of repetitions. Like synapses in the human brain, the more the MTJs are called upon, the higher the chance that the information will be recorded. The simulations performed by the researchers show that, unlike existing memory systems, theirs can efficiently — i.e. quickly while using little energy — resolve cognitive tasks such as image or video analysis.  
The scientists can now take up a new challenge by developing the first prototype of this neuro-inspired computer memory system.


Future of Flight Taking Shape: Electric Motors and Shape-Changing Wings Pass Tests

A passenger seated over the wing of a future airplane might have a very different view out the window if two recently completed NASA flight tests are any indication. 

Last week the agency announced a large model aircraft equipped with 10 electric motors attached to rotatable wings and rear tailplanes had successfully completed an initial test flight. If all goes well, the new design could make next-generation unmanned aerial vehicles for long-endurance military, agriculture and other monitoring missions. A personal aerial vehicle version of the design could also carry up to four people when scaled.

Meanwhile, a separate team announced equally satisfactory airworthiness results for a jet equipped with wings built without traditional flaps. Instead, the jet flew 22 research flights with monolithic wings whose trailing edges bend and flex. The morphing wings have been cleared to be built into future large transport aircraft. See pictures and learn more about the two advances below.

Keep reading

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New illustration for my Women in Science series. Hedy Lamarr was a glamorous movie star and genius inventor. She co-invented the technology that allowed the U.S military to use FHSS radio signals control torpedoes during WWII. She is in the National Inventors Hall of Fame for this achievement and her technology is used for bluetooth and Wi-FI today!

Check it out here:

Seeking SciNote, Computer Science: How Do Random Number Generators Work?


How do random number generators work?

Asked by anonymous.


Great question. To start everyone out on the same page, a random number generator (RNG) is a physical or computational device designed to generate a random sequence of numbers, sometimes called a string. The simplest kinds of RNGs use classical physics or mathematics to generate what are called pseudo-random numbers. These are produced using a predictable formula or model, but are good enough for most applications because the strings produced are a fair approximation of randomness. Numbers generated in this way run the risk that a sufficiently skilled intruder can reverse-engineer the algorithm from the series of numbers produced. There are also what are called chaotic number generators, which work by measuring small differences in complex systems, like pictures (or the last digit of a number in the stock market) to produce their pseudo-random numbers. Though these can be more unpredictable than numbers generated by an algorithm, they can still be replicated in principle by anyone with access to the generating system. This is the case for all pseudo-random numbers: they can be reproduced by anyone with enough access and/or diligence because they are not truly random. For help understanding the difference between random and non-random number generators, see this video: .

The only way to produce truly random strings is to take advantage of quantum mechanics, using a quantum random number generator. All quantum processes have some inherent amount of indeterminacy,  meaning that the outcome is in principle impossible to exactly predict, and indeterminacy is the key to generating random numbers. And while there have been ways of using things like radioactive decay for this goal, in 2009-2010 physicist Chris Monroe led a quantum optics team that demonstrated an interesting new method of producing a certifiably random string of numbers based on fundamental principles of quantum mechanics. Monroe’s team at the Joint Quantum Institute “placed a single atom in each of two completely isolated enclosures spaced a meter apart. They then proceeded to entangle the two atoms. Every time their apparatus signaled that entanglement had been achieved, the researchers rotated each atom on its axes according to a random schedule and then took a measurement of each atom’s emitted light. The value from each of two atoms was then used to generate a binary number” (quote from here). When the atom’s spin was “up”, it emitted a photon, and a 1 was produced. When the atom’s spin was “down”, no photon was emitted, and a 0 was produced. As Monroe says, “the outcome of any classical physical process can ultimately be determined with enough information about initial conditions. Only quantum processes can be truly random”. His team found a new way of turning the inherent randomness of quantum mechanics into a completely random binary number. More on their experiment, which is completely free from deterministic processes, can be found at For more on the difference between classical and quantum RNGs, see .

Why do we want random numbers in the first place? The quest for an ideal random number generator has largely been driven by the need to encode messages. Encryption can ensure that only users who have access to the appropriate key will get access to the data in a system. To give just one example of how important encryption is, with a single system of access keys that were encrypted by random numbers, the governments of the United States and United Kingdom were able to access the cell phones of billions of people worldwide. According to an article on The Intercept, “With these stolen encryption keys, intelligence agencies can monitor mobile communications without seeking or receiving approval from telecom companies and foreign governments. Possessing the keys also sidesteps the need to get a warrant or a wiretap, while leaving no trace on the wireless provider’s network that the communications were intercepted”. The author also writes that “the theft of encryption keys from major wireless network providers is tantamount to a thief obtaining the master ring of a building superintendent who holds the keys to every apartment.” Numbers generated through classical RNGs are much more prone to this kind of theft than numbers generated quantum mechanically, because accessing all of the former only requires knowledge of the algorithm used to produce them. With quantum mechanically-generated random numbers, each has to be individually known because there is no way to get from one to another; an intruder trying to guess a second number based on a first would do no better than chance. In a society where random numbers are used to protect sensitive information, it is crucial that these numbers are truly random, or at the very least pseudo-random in irreversible ways, because more random numbers make for more secure systems.

“Random numbers certified by Bell’s theorem,” S. Pironio, A. Acín, S. Massar, A. Boyer de la Giroday, D.N. Matsukevich, P. Maunz, S. Olmschenk, D. Hayes, L. Luo, T.A. Manning, C. Monroe, Nature, 464, 1021–4 (2010).
“Random Numbers – But Not By Chance”,
“Generating Random Numbers.” Random Numbers Info. University of Geneva and IDQ.

Answered by: John M., Expert Leader
Edited by: Brendan C., Editor