Chips

By Richard Adhikari
TechNewsWorld

Twenty years from now, semiconductor chips will cost a penny apiece and will be in everything -- our clothing, our sunglasses, our contact Increase sales with VerticalResponse. Free trial. lenses and even our toilets -- physicist Michio Kaku told an audience Thursday at the RSA 2011 convention.

They'll revolutionize warfare, manufacturing and the medical field, said Kaku, one of the co-founders of string field theory.
Kaku's talk was based on interviews he conducted with 300 of the world's top scientists about their views of the future. Those interviews have been published in his latest book, Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100.

Moore's Law, which posits that computing power doubles roughly every 18 months, also more or less applies to electricity, running water and paper, Kaku suggested.
Extrapolating this curve out into the future, some computer chips will eventually cost about a penny apiece, Kaku stated. "There will be millions scattered in our clothing, in the environment."
When computer chip prices fall to about a penny, chips will be ubiquitous. They'll be present in our clothing and in our eyeglasses, Kaku remarked.
Embedded chips will give our eyeglasses access to the Internet on the fly, Kaku predicted. Eyeglasses will then double as their owners' home offices or entertainment centers.
"In 10 years, we'll live in a world where we can identify people's faces, see their biographies, see subtitles as they speak in other languages and, if you're an artist, you'll be able to create any sculpture by waving your hands and see it develop," Kaku said.
The Technology of War
The United States military is using a version of smart glasses called "Land Warrior," which Kaku saw at demonstrated at Fort Benning. "It's the Internet of the battlefield -- you'll see friendly troops, enemy troops, everything on your glasses," he told his audience.
Augmented reality will be used to enhance the capability of smart glasses. "You've seen this before in the movies," Kaku said, showing a slide from the film "The Terminator."
The Chinese already have created an augmented reality version of the Summer Palace in Beijing, Kaku stated.
The U.S. Department of Defense began investing in augmented technology back in 2007.
One company, Tanagram, has worked on a DARPA project involving an augmented reality interface for F-35 fighter jets.
Two other companies announced products including augmented reality at the Mobile World Congress, being held in Barcelona, Spain.
Route 66 unveiled a new app for Android earlier this week that incorporates augmented reality. Meanwhile, Travel site TripAdvisor announced Thursday at MWC that it has added a virtual tours feature that uses augmented reality to its iPad app.
More Uses for the Ubiquitous Chip
Paper will be electronic, using organic LED (OLED) technology. "Your cellphone will scroll out paper which can be totally flexible," Kaku said.
The wallpaper of the future will be both flexible and intelligent, and it will consist of hundreds of computers on the wall. "When you want to redecorate your house, you talk to the walls and they'll change to whatever color you want," Kaku said.
Office files will follow people around in the cloud as they move from room to room, or between the home and office, Kaku forecast.
"Today your office is structured around your PC," Kaku said. "Why? The content is more important than your platform."
Cars of the future will drive themselves, Kaku said. "Google is already investing millions of dollars into this technology and predicts that, in eight or nine years, your car will drive itself," he pointed out.
Cars of the future will drive themselves
Cars of the future will drive themselves, Michio Kaku stated. The research is already being undertaken.
Google (Nasdaq: GOOG), however, isn't hold too tightly to that time frame.
"We aren't going to put a hard number on when the car will be available on the road," Google spokesperson Jay Nancarrow told TechNewsWorld.
"The technology has made a lot of progress, but there are a lot of other external factors involved with putting cars on the road that make it more difficult to give an estimate," he explained. "The team is hard at work on some of the toughest computer science challenges in their field."
Reinventing Manufacturing
The ubiquity of chips will lead to mass customization, Kaku forecast.
For example, consumers will be able to walk into a store, select an item of clothing they want, and send a message to the manufacturer, who will create that item tailored exactly to them, Kaku said.
This is already happening now, Jake Sorofman, chief marketing officer at rPath, told TechNewsWorld.
"That's what Dell's (Nasdaq: DELL) doing," Sorofman elaborated. "They've outsourced manufacturing and they're just assemblers who configure their products to suit market needs. We see that at Ikea as well."
Eliminating Diseases
All medicine will be reduced to computer science, Kaku said.
"When Isaac Asimov did this in the movie 'Fantastic Voyage,' people laughed," Kaku elaborated. "In the future we'll do this. We've already done this with nanoparticles."
The California Institute of Technology has developed nanoparticles that can home in and destroy individual cancer cells, Kaku said. These have been up to 80 percent effective in trials.
Smart toilets will help eliminate cancer, Kaku suggested. "Your toilet will have DNA chips which can zoom in on individual cancer cells and can tell you if you have cancer," he explained. "It will tell you that there are 100 cancer cells in a colony in your body 10 years before that forms into a tumor."



Source: Technewsworld

Demystifying the memristor:

Proof of fourth basic circuit element could transform computing

By Jamie Beckett, April 2008 Researchers at HP Labs have solved a decades-old mystery by proving the existence of a fourth basic element in integrated circuits that could make it possible to develop computers that turn on and off like an electric light. The memristor — short for memory resistor - could make it possible to develop far more energy-efficient computing systems with memories that retain information even after the power is off, so there's no wait for the system to boot up after turning the computer on. It may even be possible to create systems with some of the pattern-matching abilities of the human brain. A mathematical model and a physical example that prove the memristor's existence appear in a paper published in the April 30 issue of the journal Nature. "To find something new and yet so fundamental in the very mature field of electrical engineering is a big surprise," said R. Stanley Williams, an HP Senior Fellow and director of the Information and Quantum Systems Lab (IQSL). Fundamental circuit element The memristor first appeared in a 1971 paper published by Professor Leon Chua, a distinguished faculty member in the Electrical Engineering and Computer Sciences Department of the University of California Berkeley. Chua described and named the memristor, arguing that it should be included along with the resistor, capacitor and inductor as the fourth fundamental circuit element. The memristor has properties that cannot be duplicated by any combination of the other three elements. Although researchers had observed instances of memristance for more than 50 years, the proof of its existence remained elusive - in part because memristance is much more noticeable in nanoscale devices. The crucial issue for memristance is that the device' atoms need to change location when voltage is applied, and that happens much more easily at the nanoscale. Proving memristor in the lab Williams and co-authors Dmitri B. Strukov, Gregory S. Snider and Duncan R. Stewart were able to formulate a physics-based model of a memristor and build nanoscale devices in their lab that demonstrate all of the necessary operating characteristics to prove that the memristor was real. "This is an amazing development," Chua says. "It took someone like Stan Williams with a multi-disciplinary background and deep insights to conceive of such a tiny memristor only a few atoms in thickness." Williams has a background in physical chemistry. Strukov is a theoretical physicist, Snider is a computer architect and Stewart is an experimental physicist.   Possible replacement for D-RAM By providing a mathematical model for the physics of a memristor, the team makes possible for engineers to develop integrated circuit designs that take advantage of its ability to retain information. "This opens up a whole new door in thinking about how chips could be designed and operated," Williams says. Engineers could, for example, develop a new kind of computer memory that would supplement and eventually replace today's commonly used dynamic random access memory (D-RAM). Computers using conventional D-RAM lack the ability to retain information once they are turned off. When power is restored to a D-RAM-based computer, a slow, energy-consuming "boot-up" process is necessary to retrieve data stored on a magnetic disk required to run the system. Memristor-based computers wouldn't require that process, using less power and possibly increasing system resiliency and reliability. Chua believes the memristor could have applications for computing, cell phones, video games - anything that requires a lot of memory without a lot of battery-power drain. Brain-like systems? As for the human brain-like characteristics, memristor technology could one day lead to computer systems that can remember and associate patterns in a way similar to how people do. This could be used to substantially improve facial recognition technology or to provide more complex biometric recognition systems that could more effectively restrict access to personal information. These same pattern-matching capabilities could enable appliances that learn from experience and computers that can make decisions. Nanoscale electronics experience In the memristor work, the researchers built on their extensive experience - Williams founded the precursor lab to IQSL in 1995 - in building and studying nanoscale electronics and architectures. One goal of this work has been to move computing beyond the physical and fiscal limits of conventional silicon chips. For decades, increases in chip performance have come about largely by putting more and more transistors on a circuit. Higher densities, however, increase the problems of heat generation and defects and affect the basic physics of the devices. "Instead of increasing the number of transistors on a circuit, we could create a hybrid circuit with fewer transistors but the addition of memristors - and more functionality," Williams says. Alternately, memristor technologies could enable more energy-efficient high-density circuits. In 2007, the team developed an architecture for such a hybrid chip using conventional CMOS technology and nanoscale switching devices. "What we now know," Williams says, "is that these switches have a name - memristor." Source: HP  Nvidia GeForce GTX 590 3 GB Review: Firing Back With 1024 CUDA Cores 8:00 AM - March 24, 2011 by Chris Angelini AMD shot for—and successfully achieved—the coveted “fastest graphics card in the world” title with its Radeon HD 6990. Now, Nvidia is gunning for that freshly-claimed honor with a dual-GF110-powered board that speaks softly and carries a big stick. In this corner... Today, the worst-kept secret in technology officially gets the spotlight. Hot on the heels of AMD’s Radeon HD 6990 4 GB introduction three weeks ago, Nvidia is following up with its GeForce GTX 590 3 GB. According to Nvidia, it could have introduced this card more than a month ago. However, we know it continued revising its plans for a new flagship well into March. The result is a board deliberately intended to emphasize elegance, immediately after the Radeon HD 6990 bludgeoned us over the head with abrasive acoustics. Pursuing quietness might sound ironic, given that GPUs based on Nvidia’s Fermi architecture are notoriously hot and power-hungry. To think the company could put two on a single PCB and not out-scream AMD’s dual-Cayman-based card is almost ludicrous. And yet, that’s what Nvidia says it did. It admits that getting there wasn’t an easy task, though. Compromises were made. For example, Nvidia uses the same mid-mounted fan design for which we chided AMD. It dropped the clocks on its GPUs to help keep thermals under control. And the card still uses more power than any graphics product we’ve ever tested. And in the other corner... But it’s quiet. Crazy-freaking quiet. The quietest dual-GPU board I’ve tested since ATI’s Rage Fury Maxx (how’s that for back-in-the-day?). Mission accomplished on that front. The question remains, though: was Nvidia forced to give up the farm just to show AMD that hot cards don't have to make lots of noise? Under The Hood: Dual GF110s, Both Uncut In my discussions with Nvidia, the company made it clear that it wanted to use two GF110 processors, and it didn’t want to hack them up. Uncut GF110s, as you probably already know from reading GeForce GTX 580 And GF110: The Way Nvidia Meant It To Be Played, employ four Graphics Processing Clusters, each with four Streaming Multiprocessors. You’ll find 32 CUDA cores in each SM, totaling 512 cores per GPU. Each SM also offers four texturing units, yielding 64 across the entire chip. Of course, there’s one Polymorph engine per SM as well, though as we’ve seen in the past, Nvidia’s approach to parallelizing geometry doesn’t necessarily scale very well. As in our GTX 580 review, GF110 doesn't get cut-back here The GPU’s back-end features six ROP partitions, each capable of outputting eight 32-bit integer pixels at a time, adding up to 48 pixels per clock. An aggregate 384-bit memory bus is divisible into a sextet of 64-bit interfaces, and you’ll find 256 MB of GDDR5 memory at all six stops. That adds up to 1.5 GB of memory per GPU, which is how you arrive at the GeForce GTX 590’s 3 GB. Nvidia ties GTX 590’s GF110 processors together using its own NF200 bridge, which takes a single 16-lane PCI Express 2.0 interface and multiplexes it out to two 16-lane paths—one for each GPU. GeForce GTX 590 GeForce GTX 580 Radeon HD 6990 Radeon HD 6970 Radeon HD 6950 Manufacturing Process 40 nm TSMC 40 nm TSMC 40 nm TSMC 40 nm TSMC 40 nm TSMC Die Size 2 x 520 mm² 520 mm² 2 x 389 mm² 389 mm² 389 mm² Transistors 2 x 3 billion 3 billion 2 x 2.64 billion 2.64 billion 2.64 billion Engine Clock 607 MHz 772 MHz 830 MHz 880 MHz 800 MHz Stream Processors / CUDA Cores 1024 512 3072 1536 1408 Compute Performance 2.49 TFLOPS 1.58 TFLOPS 5.1 TFLOPS 2.7 TFLOPS 2.25 TFLOPS Texture Units 128 64 192 96 88 Texture Fillrate 77.7 Gtex/s 49.4 Gtex/s 159.4 Gtex/s 84.5 Gtex/s 70.4 Gtex/s ROPs 96 48 64 32 32 Pixel Fillrate 58.3 Gpix/s 37.1 Gpix/s 53.1 Gpix/s 28.2 Gpix/s 25.6 Gpix/s Frame Buffer 2 x 1.5 GB GDDR5 1.5 GB GDDR5 2 x 2 GB GDDR5 2 GB GDDR5 2 GB GDDR5 Memory Clock 853 MHz 1002 MHz 1250 MHz 1375 MHz 1250 MHz Memory Bandwidth 2 x 163.9 GB/s (384-bit) 192 GB/s (384-bit) 2 x 160 GB/s (256-bit) 176 GB/s (256-bit) 160 GB/s (256-bit) Maximum Board Power 365 W 244 W 375 W 250 W 200 W What changed from the ill-received GF100-based GeForce GTX 480 to GF110? From my GeForce GTX 580 review: “The GPU itself is largely the same. This isn’t a GF100 to GF104 sort of change, where Shader Multiprocessors get reoriented to improve performance at mainstream price points (read: more texturing horsepower). The emphasis here remains compute muscle. Really, there are only two feature changes: full-speed FP16 filtering and improved Z-cull efficiency. GF110 can perform FP16 texture filtering in one clock cycle (similar to GF104), while GF100 required two cycles. In texturing-limited applications, this speed-up may translate into performance gains. The culling improvements give GF110 an advantage in titles that suffer lots of overdraw, helping maximize available memory bandwidth. On a clock-for-clock basis, Nvidia claims these enhancements have up to a 14% impact (or so).” That's a 12-layer PCB with 10-phase power, and NF200 in the middle Other than that, we’re still talking about two pieces of silicon manufactured on TSMC’s 40 nm node and composed of roughly 3 billion transistors each. At 520 square millimeters, GF110 is substantially larger than AMD’s Cayman processor, which measures 389 mm² and is made up of 2.64 billion transistors. Now, it’s great to get all of those resources (times two) on GeForce GTX 590. However, while the GeForce GTX 580 employs a 772 MHz graphics clock and 1002 MHz memory clock, the GPUs on GTX 590 slow things down to 607 MHz and 853 MHz, respectively. As a result, this card’s performance isn’t anywhere near what you’d expect from two of Nvidia’s fastest single-GPU flagships. That might be alright, though. After all, AMD launched Radeon HD 6970 as a GeForce GTX 570-contender; the 580 sat in a league of its own. So, although AMD’s Radeon HD 6990 comes very close to doubling the performance of the company’s quickest single-GPU cards, GeForce GTX 590 doesn’t have to do the same thing in order to be competitive at the $700 price point AMD already established and Nvidia plans to match. We already know what AMD had to do in order to deliver “the fastest graphics card in the world.” Now, how does Nvidia counter? Read Full Here