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  1. Hi Guyz, Just Got PES 2014. Am having issues with play this game because of this Any idea how I can increase it?
  2. Hi Nsane community :hi: , I have MSI gaming notebook with 8 GB of Nanya RAM (4 * 2) .After about eight month of purchasing date , I got a problem with one of the RAM #1 which cause BSOD (MEMORY MANAGEMENT) .I did memory diagnostic on Windows 7 and the result was "Hardware problem was detected .." . Why this happen ? I did't make any thing wrong with my laptop ,(I mean I really care about its temperature and keep it clean from dust ..) By the way The laptop is still working well (running games , programs ,NET ) except sometimes I get the mentioned BSOD. :please: Is there any one knows about or faced like that problem ? And what do you advise me to do about my sick ram ? :( Solved : I brought my laptop to MSI service center to fix it .. Staff there told me that both of RAMs have a problem .They replaced RAMs with new same brand RAM (NANYA) .When I asked them about what was the problem ,they just told me that "Unexpected error occurred !" The important thing my laptop is now working like charm without any annoying BSOD :D
  3. IBM has demonstrated a new type of memory technology that the company believes could one day be a replacement for NAND flash. The company’s Theseus Project (conducted in cooperation with the University of Patras in Greece) is the first attempt to combine phase change memory, conventional NAND, and DRAM on a single controller. The result? A hybridized storage solution that outperforms PCIe-based SSDs by between 12 and 275 times. The physics of phase change Phase change memory is one of a number of alternative memory structures that’s beenproposed as a replacement for NAND. Phase change memory works by rapidly heating chalcogenide glass, shifting it between its crystalline and amorphous state. In its amorphous state (read as a binary 0), the structure has very high resistance, while in its crystalline state (binary 1) resistance is quite low. Phase change memory can quickly shift between the two states, plus research from Intel and Micron have demonstrated the feasibility of intermediate states, which allows two bits of information to be stored per cell. Phase change memory has much lower latency than NAND, much faster read/write times (in theory), and it can withstand millions of write cycles as compared to 30,000 with high-end SLC NAND and as few as 1,000 with TLC NAND. Even better, it’s well positioned compared to other theoretical memory devices. Even so, NAND flash has enormous economies of scale and billions invested in fab plants across the world. What IBM has done with Theseus is to incorporate a small amount of PCM into a hybrid structure where its ultra-low-latency characteristics can be effectively leveraged. This chart shows the various areas where IBM believes phase change memory could be useful. Note that in many cases, the PCM is being integrated either as a cache solution or as an additional tier of storage between NAND and DRAM, just as NAND is often integrated between DRAM and a conventional hard drive. Project Theseus is an aggregate controller featuring what appears to be 2.8GB of PCM (36 128Mbit cells per card, 5 cards total). IBM calls this its PSS (Prototype Storage Solution). The advantages of PCM are illustrated in the slides above. These graphs show the total latency for various types of requests. Note that the PSS solution (that’s the PCM card) completes the overwhelming majority of its requests in under 500 microseconds. The two MLC solutions top out at 14,000 and 20,000 microseconds compared to 2,000 microseconds for the PSS, while the TLC NAND is an order of magnitude slower, topping out at 120,000 microseconds. In short, these early PCMs, built on 90nm CMOS and at extremely low density (modern NAND flash is now available in 512Gbit sizes compared to 128Mbit for PCM) is a full order of magnitude faster than commercial NAND, with vastly superior write performance and data longevity. There’s just one little problem IBM makes a point of noting that its PSS solution uses 90nm memory produced by Micron. The only problem? Micron gave notice earlier this year that it was cancelling all of its PCM production and pulling out of the industry. While it left open the door to revisiting the memory tech at some point in the future, it indicated that the superior scaling of 3D NAND was a better option (despite the numerous problems identified with that technology in the short term). Where does this leave PCM? The 2013 ITRS report notes that NAND performance isn’t actually expected to increase much from present levels — in fact, it’s going to be difficult to maintain current NAND performance while improving density and holding write endurance constant. Right now, PCM is the most promising next-generation memory technology on the market — but if no one steps forward to manufacture it, it’s going to be a tough sell. Source
  4. PC DDR4 pricing back to where it was in Oct 2016. Further declines expected in Q1 and Q2. Technology market intelligence firm TrendForce, or more precisely its DRAMeXchange division, has published what looks to be some good news for PC enthusiasts. Last year we saw signs that this might happen, but now some trends have been set in motion which should deliver "significant price declines for DRAM products during 1H19". In brief, there are both seasonal and oversupply factors in play here and it is possible that we will see PC DRAM price decline of 20 per cent or so by the end of Q1 2019. DRAMeXchange notes that Contract prices of DRAM products across all major application markets already registered declines of more than 15 per cent month-on-month in January, and they will continue their descent in February and March. If that is really the case then a 20 per cent drop in Q1 looks to be a guarded estimate. Looking ahead to Q2 2019 DRAMeXchange reckons the oversupply situation will persist and mainstream DRAM products will drop by an average further 15 per cent in this period. We all know new tech, especially new smartphone releases, can eat up available DRAM supply but demand related to 5G, AIoT, IIoT, and automotive electronics still is in the growth stage, while the smartphone market has decelerated as people hold onto mobiles for longer due to lack of innovation. It remains to be seen if Android phone makers can produce devices compelling enough at the upcoming MWC to spur a flurry of updates. Considering PC market specifics, DRAMeXchange observes that the average contract price of mainstream 8GB PC DRAM modules is on its way to under US$45 at the time of reporting. Similarly server RAM oversupply has affected the market, in this case even more severely as it is expected to see a price decline of nearly 30 per cent QoQ. PC DDR4 pricing in the UK today All the above talk of contract and spot prices of DRAM might seem a little detached from what we actually pay for PC memory modules. Therefore it is worth a look at the trends on sites like CamelCamelCamel, which back up the analyst charts, and trends. For example, here in the UK, Crucial 4GB DDR4 2400 MT/s memory modules are at their cheapest price since October 2016, at £24.28. I also checked out the Corsair Vengeance LPX 8GB DDR4 2400MHz C16 module pricing. As you can see that is also trending nicely for would-be buyers at £48.62. Again, this is probably the best pricing we have seen for these modules since October 2016. Should you therefore wait a little longer if you are thinking about upgrading your PC RAM? According to the TrendForce analysts, it would seem like the answer to that is yes. No advice intended, remember 'stuff' happens, and the GBP could move very strongly one way or another in the coming weeks, for example. View: Original Article.
  5. Why can’t I remember? Model may show how recall can fail Model may predict why you can’t recall what you know you remember. Enlarge Serdar Acar / EyeEm Physicists can create serious mathematical models of stuff that is very far from physics—stuff like biology or the human brain. These models are hilarious, but I'm still a sucker for them because of the hope they provide: maybe a simple mathematical model can explain the sexual choices of the disinterested panda? (And, yes, I know there is an XKCD about this very topic). So a bunch of physicists who claimed to have found a fundamental law of memory recall was catnip to me. To get an idea of how interesting their work is, it helps to understand the unwritten rules of “simple models for biology.” First, the model should be general enough that the predictions are vague and unsatisfying. Second, if you must compare with experimental data, do it on a logarithmic scale so that huge differences between theory and experiment at least look tiny. Third, if possible, make the mathematical model so abstract that it loses all connection to the actual biology. By breaking all of these rules, a group of physicists has come up with a model for recall that seems to work. The model is based on a concrete idea of how recall works, and, with pretty much no fine-tuning whatsoever, it provides a pretty good prediction for how well people will recall items from a list. Put your model on the catwalk It's widely accepted that memories are encoded in networks of neurons. We know that humans have a remarkable capacity to remember events, words, people, and many other things. Yet some aspects of recall are terrible. I’ve been known to blank on the names of people I’ve known for a decade or more. But even simpler challenges fail. Given a list of words, for instance, most people will not recall the entire list. In fact, a remarkable thing happens. Most people will start by recalling words from the list. At some point, they will loop back and recall a word they’ve already said. Every time this happens, there is a chance that it will trigger another new word; alternately, the loop could start to cycle over other words already recalled. The more times a person loops back, the higher the chance that no new words will be recalled. Based on these observations, the researchers created a model based on similarity. Each memory is stored in a different but overlapping network of neurons. Recall jumps from a starting point to the next item that has the greatest network overlap with the previous item. The process of recall suppresses the jump back to the item that had just been recalled previously, which would have the most overlap. By using those simple rules, recall follows a trajectory that loops back on itself at some random interval. However, if recall were completely deterministic, the first loop back to a word that was already recalled would result in an endless repetition of the same few items. To prevent this, the model is probabilistic, not deterministic: there is always a chance of jumping to a new word and breaking out of a loop. Boiling all this down, the researchers show that, given a list of items of a known length, the model predicts the average number of items that can be recalled. There is no fine-tuning here at all: if you take the model above and explore the consequences, you get a fixed relationship between list length and number of items recalled. That's pretty amazing. But is it true? Experiments are messy At first sight, some experiments immediately contradict the researcher’s model. For instance, if the subject has a longer period of time to look at each word on the list, they will recall more words. Likewise, age and many other details influence recall. But the researchers point out that their model assumes that every word in the list is stored in memory. In reality, people are distracted. They may miss words entirely or simply not store the words they see. That means that the model will always overestimate the number of words that can be recalled. To account for this, the researchers performed a second set of experiments: recognition tests. Some subjects did a standard recall test. They were shown a list of words sequentially and asked to recall as many words as possible. Other subjects were shown a list of words sequentially, then shown words in random order and asked to choose which words were on the list. The researchers then used their measured recognition data to set the total number of words memorized. With this limit, the agreement between their theoretical calculations and experiments is remarkable. The data seems to be independent of all parameters other than the length of the list, just as the model predicts. The result also seems to tell us that the variation in experimental data observed in previous experiments is not in recall but in memorization. A delicate point So what does the model tell us? It may provide some insight into the actual mechanisms of recall. It may also point to how we can construct and predict the behavior of neural-network-based memories. But (and maybe this is my failure of imagination) I cannot see how you would actually use the model beyond what it already tells us. Physical Review Letters, 2020, DOI: 10.1103/PhysRevLett.124.018101 (About DOIs) Source: Why can’t I remember? Model may show how recall can fail (Ars Technica)
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