Intel Core i9

Intel recently announced a new family of processors for enthusiasts, the Core X-series, and it’s anchored by the company’s first 18-core CPU, the i9-7980XE.



Priced at $1,999, the 7980XE is clearly not a chip you’ll see in an average desktop. Instead, it’s more of a statement from Intel. It beats out AMD’s 16-core Threadripper CPU, which was slated to be that company’s most powerful consumer processor for 2017. And it gives Intel yet another way to satisfy the demands of power-hungry users who might want to do things like play games in 4K while broadcasting them in HD over Twitch. And, as if its massive core count wasn’t enough, the i9-7980XE is also the first Intel consumer chip that packs in over a teraflop’s worth of computing power.




If 18 cores are overkill for you, Intel also has other Core i9 Extreme Edition chips in 10-, 12-, 14- and 16-core variants. Perhaps the best news for hardware geeks: The 10 Core i9-7900X will retail for $999, a significant discount from last year’s version.

All of the i9 chips feature base clock speeds of 3.3GHz, reaching up to 4.3GHz dual-core speeds with Turbo Boost 2.0 and 4.5GHz with Turbo Boost 3.0 a new version of Turbo Boost which Intel has upgraded. The company points out that while the additional cores on the Core X models will improve multitasking performance, the addition of technologies like Turbo Boost Max 3.0 ensures that each core is also able to achieve improved performance. (Intel claims that the Core X series reaches 10 percent faster multithread performance over the previous generation and 15 percent faster single thread.)



(via Engadget, The Verge)


Google’s and Nvidia’s AI Chips


Google will soon launch a cloud computing service that provides exclusive access to a new kind of artificial intelligence chip designed by its own engineers. CEO Sundar Pichai revealed the new chip and service this morning in Silicon Valley during his keynote at Google I/O, the company’s annual developer conference.


This new processor is a unique creation designed to both train and execute deep neural networks—machine learning systems behind the rapid evolution of everything from image and speech recognition to automated translation to robotics. Google says it will not sell the chip directly to others. Instead, through its new cloud service, set to arrive sometime before the end of the year, any business or developer can build and operate software via the internet that taps into hundreds and perhaps thousands of these processors, all packed into Google data centers.

According to Dean, Google’s new “TPU device,” which spans four chips, can handle 180 trillion floating point operations per second, or 180 teraflops, and the company uses a new form of computer networking to connect several of these chips together, creating a “TPU pod” that provides about 11,500 teraflops of computing power. In the past, Dean said, the company’s machine translation model took about a day to train on 32 state-of-the-art CPU boards. Now, it can train in about six hours using only a portion of a pod.


Nvidia has released a new state-of-the-art chip that pushes the limits of machine learning, the Tesla P100 GPU. It can perform deep learning neural network tasks 12 times faster than the company’s previous top-end system (The TitanX). The P100 was a huge commitment for Nvidia, costing over $2 billion in research and development, and it sports a whopping 150 billion transistors on a single chip, making the P100 the world’s largest chip, Nvidia claims. In addition to machine learning, the P100 will work for all sorts of high-performance computing tasks — Nvidia just wants you to know it’s really good at machine learning.


To top off the P100’s introduction, Nvidia has packed eight of them into a crazy-powerful $129,000 supercomputer called the DGX-1. This show-horse of a machine comes ready to run, with deep-learning software preinstalled. It’s shipping first to AI researchers at MIT, Stanford, UC Berkeley, and others in June. On stage, Huang called the DGX-1 “one beast of a machine.”

The competition between these upcoming AI chips and Nvidia all points to an emerging need for simply more processing power in deep learning computing. A few years ago, GPUs took off because they cut the training time for a deep learning network from months to days. Deep learning, which had been around since at least the 1950s, suddenly had real potential with GPU power behind it. But as more companies try to integrate deep learning into their products and services, they’re only going to need faster and faster chips.


(via Wired, Forbes, Nvidia, The Verge)


Artificial Intelligence ready ARM CPUs (DynamIQ)

ARM processors are ubiquitous and many of the tech gadgets we used are powered by them, furthermore, the company is showing off its plans for the future with DynamIQ. Aimed squarely at pushing the artificial intelligence and machine learning systems we’re expecting to see in cars, phones, gaming consoles and everything else, it’s what the company claims is an evolution on the existing “big.Little” technology.


Here’s a high-level look at some of the new features, capabilities and benefits DynamIQ will bring to new Cortex-A processors later this year:

  • New dedicated processor instructions for ML and AI: Cortex-A processors designed for DynamIQ technology can be optimized to deliver up to a 50x boost in AI performance over the next 3-5 years relative to Cortex-A73-based systems today and up to 10x faster response between CPU and specialized accelerator hardware on the SoC that can unleash substantially better-combined performance.


  • Increased multi-core flexibility: SoC designers can scale up to eight cores in a single cluster and each core can have different performance and power characteristics. These advanced capabilities enable faster responsiveness to ML and AI applications. A redesigned memory subsystem enables both faster data access and enhance power management
  • More performance within restricted thermal budgets: Efficient and much faster switching of software tasks to match the right-sized processor for optimal performance and power is further enhanced through independent frequency control of individual processors
  • Safer autonomous systems: DynamIQ brings greater levels of responsiveness for ADAS solution and increased safety capabilities which will enable partners to build ASIL-D compliant systems for safe operation under failure conditions.



(source: ARM community, Engadget)

Quantum computer memories of higher dimensions than a qubit

A quantum computer memory of higher dimensions has been created by the scientists from the Institute of Physics and Technology of the Russian Academy of Sciences and MIPT by letting two electrons loose in a system of quantum dots. In their study published in Scientific Reports, the researchers demonstrate for the first time how quantum walks of several electrons can help for implementation of quantum computation.

For more information: Quantum Computing


Abstraction – Walking Electrons

“By studying the system with two electrons, we solved the problems faced in the general case of two identical interacting particles. This paves the way toward compact high-level quantum structures,” says Leonid Fedichkin, associate professor at MIPT’s Department of Theoretical Physics.

In a matter of hours, a quantum computer will be able to hack into the most popular cryptosystem used by web browsers. As far as more benevolent applications are concerned, a quantum computer would be capable of molecular modeling that accounts for all interactions between the particles involved. This, in turn, would enable the development of highly efficient solar cells and new drugs.

As it turns out, the unstable nature of the connection between qubits remains the major obstacle preventing the use of quantum walks of particles for quantum computation. Unlike their classical analogs, quantum structures are extremely sensitive to external noise. To prevent a system of several qubits from losing the information stored in it, liquid nitrogen (or helium) needs to be used for cooling. A research team led by Prof. Fedichkin demonstrated that a qubit could be physically implemented as a particle “taking a quantum walk” between two extremely small semiconductors known as quantum dots, which are connected by a “quantum tunnel.”

The Quantum dots are like potential wells to an electron, therefore, the position of an electron can be used to encode the basis of two states of the qubits 0 or 1.


The blue and purple dots in the diagrams are the states of the two connected qudits (qutrits and ququarts are shown in (a) and (b) respectively). Each cell in the square diagrams on the right side of each figure (a-d) represents the position of one electron (i = 0, 1, 2, … along the horizontal axis) versus the position of the other electron (j = 0, 1, 2, … along the vertical axis). The cells color-code the probability of finding the two electrons in the corresponding dots with numbers i and j when a measurement of the system is made. Warmer colors denote higher probabilities. Credit: MIPT

If an entangled state is created between several qubits, their individual states can no longer be described separately from one another, and any valid description must refer to the state of the whole system. This means that a system of three qubits has a total of eight basis states and is in a superposition of them: A|000⟩+B|001⟩+C|010⟩+D|100⟩+E|011⟩+F|101⟩+G|110⟩+H|111⟩. By influencing the system, one inevitably affects all of the eight coefficients, whereas influencing a system of regular bits only affects their individual states. By implication, n bits can store n variables, while n qubits can store 2n variables. Qudits offer an even greater advantage since n four-level qudits (aka ququarts) can encode 4n, or 2n×2n variables. To put this into perspective, 10 ququarts store approximately 100,000 times more information than 10 bits. With greater values of n, the zeros in this number start to pile up very quickly.

In this study, Alexey Melnikov and Leonid Fedichkin obtain a system of two qudits implemented as two entangled electrons quantum-walking around the so-called cycle graph. The entanglement of the two electrons is caused by the mutual electrostatic repulsion experienced by like charges. Number of qudits can be created by connecting quantum dots in a pattern of winding paths and have more wandering electrons. The quantum walks approach to quantum computation is convenient because it is based on a natural process.

So far, scientists have been unable to connect a sufficient number of qubits for the development of a quantum computer. The work of the Russian researchers brings computer science one step closer to a future when quantum computations are commonplace.

(Source: Moscow Institute of Physics and Technology, 3Tags.)

World’s First Fully Programmable and Reconfigurable Quantum Computer Module

A team of researchers, led by Professor Christopher Monroe from Joint Quantum Institute (JQI) at the University of Maryland Physics , has introduced the first fully programmable and reconfigurable quantum computer module in a paper published as the cover article in the August 4 issue of the journal Nature.

For more Info: Read Quantum Computing.



Photo of an ion trap.


The new device, dubbed a module because of its potential to connect with copies of itself is made of five individual ions, charged atoms — trapped in a magnetic field, whose strength is manipulated in such a way that the ions are arranged in a line. A computer program dedicated to solving a particular problem—on five quantum bits, or qubits—the fundamental unit of information in a quantum computer. Quantum computers promise speedy solutions to some difficult problems, but building large-scale, general-purpose quantum devices is a problem fraught with technical challenges.

“For any computer to be useful, the user should not be required to know what’s inside,” said Monroe, who is also a UMD Distinguished University Professor, the Bice Zorn Professor of Physics, and a fellow of the Joint Quantum Institute and the Joint Center for Quantum Information and Computer Science. “Very few people care what their iPhone is actually doing at the physical level. Our experiment brings high-quality quantum bits up to a higher level of functionality by allowing them to be programmed and reconfigured in software.”

The reconfigurability of the laser beams is a key advantage, Debnath says. “By reducing an algorithm into a series of laser pulses that push on the appropriate ions, we can reconfigure the wiring between these qubits from the outside,” he said. “It becomes a software problem, and no other quantum computing architecture has this flexibility.”

To test the module, the team ran three different quantum algorithms, including a demonstration of a Quantum Fourier Transform (QFT), which finds how often a given mathematical function repeats. It is a key piece in Shor’s quantum factoring algorithm, which would break some of the most widely-used security standards on the internet if run on a big enough quantum computer.

Two of the algorithms ran successfully more than 90 percent of the time, while the QFT topped out at a 70 percent success rate. The team says that this is due to residual errors in the pulse-shaped gates as well as systematic errors that accumulate over the course of the computation, neither of which appear fundamentally insurmountable. They note that the QFT algorithm requires all possible two-qubit gates and should be among the most complicated quantum calculations.

The team believes that eventually more qubits—perhaps as many as 100—could be added to their quantum computer module. It is also possible to link separate modules together, either by physically moving the ions or by using photons to carry information between them.


(via Science Daily, IBT)

Quantum Processors for Single Photons

Scientists have realized a photon-photon logic gate via a deterministic interaction with a strongly coupled atom-resonator system.

A team of scientists from the Quantum Dynamics Division of Professor Gerhard Rempe has successfully realized a quantum logic gate where two light quanta play the key actors. Professor Gerhard Rempe is the director at the Max Planck Institute of Quantum Optics. This attempt was highly challenging as photons do not typically interact at all but pass each other uninterrupted


Illustration of the processes that take place during the logic gate operation: The photons (blue) successively impinge on the right onto the partially transparent mirror of a resonator which contains a single rubidium atom (symbolized by a red sphere with yellow electron orbitals). The atom in the resonator plays the role of a mediator which imparts a deterministic interaction between the two photons. The diagram in the background represents the entire gate protocol. Credit: Graphic: Stephan Welte, MPQ, Quantum Dynamics Division

In the experiment presented here two independently polarized photons impinge, in quick succession, onto a resonator which is made of two high-reflectivity mirrors. Inside a single rubidium, an atom is trapped forming a strongly coupled system with the resonator. The resonator amplifies the light field of the impinging photon at the position of the atom enabling a direct atom-photon interaction. As a result, the atomic state gets manipulated by the photon just as it is being reflected from the mirror. This change is sensed by the second photon when it arrives at the mirror shortly thereafter.


After their reflection, both photons are stored in a 1.2-kilometre-long optical fiber for some microseconds. Meanwhile, the atomic state is measured. A rotation of the first photon’s polarization conditioned on the outcome of the measurement enables the back action of the second photon on the first one. “The two photons are never at the same place at the same time and thus they do not see each other directly. Nevertheless, we achieve a maximal interaction between them,” explains Bastian Hacker, a Ph.D. student at the experiment.

The scientists envision that the new photon-photon gate could pave the way towards all-optical quantum information processing. “The distribution of photons via an optical quantum network would allow linking any number of network nodes and thus enable the setup of a scalable optical quantum computer in which the photon-photon gate plays the role of a central processing unit (CPU),” explains Professor Gerhard Rempe.



via: ScienceDaily

Intel’s 1st 10-Core Desktop CPU

Intel announced a new family of high-end desktop processors code-named Broadwell-E which packs 10 Broadwell CPU cores. This 10-cores are marketed as a part of the Core i7 6950x. The previous generation Haswell-E had 8 cores.

The prior-generation Haswell-E processor family was formed on a single chip: the eight-core Haswell-EP server processor. In its full configuration, it was sold as the $999 Core i7 5960X, with six-core variants made out of cut-down versions of that same chip.

That die measured in at 356 square millimeters in Intel’s 22-nanometer manufacturing process.



The 10-core Broadwell-E chip is simply the full 10-core Broadwell-EP server chip relabeled and repurposed as a high-end desktop processor. The eight-core model, as well as the two six-core models, are also fashioned out of this same processor.

The size of the die is approximately 246 square millimeters, or approximately 69% the size of the Haswell-E die that it replaces.

Intel claims that it’s twice as fast as the quad-core i7-6700k and 35% faster than the previous gen core i7-5960k. Editing 4k video will be 65% faster than the same quad-core chip and 25% faster than previous gen i7 processor. Gaming is 25 percent faster than the 5960X when it comes to gaming in 4K while encoding and broadcasting a 1080p Twitch stream.



Intel has indicated in the past that the wafer cost increase, or effectively the cost per area of silicon, was approximately 30% in going from 22-nanometer to 14-nanometer. This means that all else equal, 246 square millimeters of 14-nanometer silicon should cost about the same as approximately 320 square millimeters of 22-nanometer silicon.

Right off the bat, it would seem that the 10-core Broadwell-E is actually cheaper to manufacture relative to the eight-core Haswell-E.

However, it’s important to note that Intel saw a decline in the gross profit margins of its data center business as a result of 14-nanometer yields relative to 22-nanometer yields.

  1. Broadwell-E: 246 square millimeter die, the defect density of 0.2 defects/square centimeter, $9,100 wafer cost.
  2. Haswell-E: 356 square millimeter die, the defect density of 0.1 defects/square centimeter, $7,000 wafer cost.

For the Broadwell-E part, under the above assumptions, 139 of the 223 dies that come off the wafer are good. For the Haswell-E part, of the 153 dies on the wafer, 109 come out good.

Based on this analysis, the raw die cost of the 14-nanometer part should be around $83. The cost of the 22-nanometer part under these assumptions works out to around $64.

The new Extreme Edition of i7 processors will also be available in an 8-core version (the i7-6900K for $1,089) and 6-core variants (the $617 i7-6850K and the $434 i7-6800K). Naturally, they’re completely unlocked, so you can overclock them to your heart’s content. All of the new chips also support DDR4-2400 RAM, a slight bump in speeds from the previous-gen processors. Intel is charging around $1,750 for the 6950X, compared to around $1,000 for the 5960X.


x series comparison.png




(sources: Engadget, Fool)