I was watching a recent ACM Turing Lecture by Hennessy and Patterson and was intrigued by a stat they cited on the cost of small chip tape-outs. They claimed that you can tape-out 100 1 mm x 1mm chips at 28 nm process node for $14,000, presumably on a test shuttle.
My question is, if I wanted to fill this chip area with MAC units (say 16 or 32 bit), how many simultaneous MACs could I do per cycle?
Just as a back of the envelope calculation, this paper describes a 32x32->64 multiplier as being 435um*482um in Synopsys' 90nm educational technology. If you just trivially scale to 28nm, you get 0.02mm^2 per instance. That's probably within an order of magnitude, which is good enough because "multipliers per mm" isn't really a meaningful metric: the interesting part is how to get data into and and out of such a multiplier array, which will dominate the area of the actual multipliers.
For another reference, the FU540-C000 is 30mm^2 in TSMC's 28nm HPC process. Yunsup's HotChips presentation from last year shows a fairly detailed die plot on page 17, from which you can calculate what 1mm^2 gets you on a modern technology -- it's quite a bit of SRAM/logic, but not many pads.
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I run an infectious disease spread model similar to "VIRUS" model in the model library changing the "infectiousness".
I did 20 runs each for infectiousness values 98% , 95% , 93% and the Maximum infected count was 74.05 , 73 ,78.9 respectively. (peak was at tick 38 for all 3 infectiousness values)
[I took the average of the infected count for each tick and took the maximum of these averages as the "maximum infected".]
I was expecting the maximum infected count to decrease when the infectiousness is reduced, but it didn't. As per what I understood this happens, because I considered the average values of each simulation run. (It is like I am considering a new simulation run with average infected count for each tick ).
I want to say that, I am considering all 20 simulation runs. Is there a way to do that other than the way I used the average?
In the Models Library Virus model with default parameter settings at other values, and those high infectiousness values, what I see when I run the model is a periodic variation in the numbers three classes of person. Look at the plot in the lower left corner, and you'll see this. What is happening, I believe, is this:
When there are many healthy, non-immune people, that means that there are many people who can get infected, so the number of infected people goes up, and the number of healthy people goes down.
Soon after that, the number of sick, infectious people goes down, because they either die or become immune.
Since there are now more immune people, and fewer infectious people, the number of non-immune healthy grows; they are reproducing. (See "How it works" in the Info tab.) But now we have returned to the situation in step 1, ... so the cycle continues.
If your model is sufficiently similar to the Models Library Virus model, I'd bet that this is part of what's happening. If you don't have a plot window like the Virus model, I recommend adding it.
Also, you didn't say how many ticks you are running the model for. If you run it for a short number of ticks, you won't notice the periodic behavior, but that doesn't mean it hasn't begun.
What this all means that increasing infectiousness wouldn't necessarily increase the maximum number infected: a faster rate of infection means that the number of individuals who can infected drops faster. I'm not sure that the maximum number infected over the whole run is an interesting number, with this model and a high infectiousness value. It depends what you are trying to understand.
One of the great things about NetLogo and some other ABM systems is that you can watch the system evolve over time, using various tools such as plots, monitors, etc. as well as just looking at the agents move around or change states over time. This can help you understand what is going on in a way that a single number like an average won't. Then you can use this insight to figure out a more informative way of measuring what is happening.
Another model where you can see a similar kind of periodic pattern is Wolf-Sheep Predation. I recommend looking at that. It may be easier to understand the pattern. (If you are interested in mathematical models of this kind of phenomenon, look up Lotka-Volterra models.)
(Real virus transmission can be more complicated, because a person (or other animal) is a kind of big "island" where viruses can reproduce quickly. If they reproduce too quickly, this can kill the host, and prevent further transmission of the virus. Sometimes a virus that reproduces more slowly can harm more people, because there is time for them to infect others. This blog post by Elliott Sober gives a relatively simple mathematical introduction to some of the issues involved, but his simple mathematical models don't take into account all of the complications involved in real virus transmission.)
EDIT: You added a comment Lawan, saying that you are interested in modeling COVID-19 transmission. This paper, Variation and multilevel selection of SARS‐CoV‐2 by Blackstone, Blackstone, and Berg, suggests that some of the dynamics that I mentioned in the preceding remarks might be characteristic of COVID-19 transmission. That paper is about six months old now, and it offered some speculations based on limited information. There's probably more known now, but this might suggest avenues for further investigation.
If you're interested, you might also consider asking general questions about virus transmission on the Biology Stackexchange site.
I am desperately searching for an efficient way - if there is one - to solve some kind of a recursive task in T-SQL (I could successfully model it in excel and on paper with an iterative solution - as many CMAs would for a small example, re-allocating shares of cost between pairs of support units serving each other in iterations and minimising the balancing unit's unallocated cost leftover to a reasonably small number to stop iterations/recursion).
Now I am trying to find a good scalable solution (or at least a feasible approach to it) how to achieve the same in T-SQL for this typical computational task in the managerial accounting area: when some internal support units service each other (and incur periodic costs, like salary etc) to produce at the end let's say 2 or 3 final products together as a firm, and as a result their respective shares of internally generated support overheads need to be reasonably (according to some physical base distribution, lets say - man hrs spent in each) allocated to these products' cost at the end of the costing exercise.
It would be quite simple if there was no reciprocal services: one support unit providing some service to other support units during the period (and a need to allocate respective costs too alongside this service qty flow) and the second and third support units doing the same thing to other support peers, before all their costs get properly berried into production costs and spread between respective products they jointly serviced (not equally for all support units, I'm using activity-based-costing approach here)... And in a real case there could be many more than just 2-3 units one could manually solve in excel or on paper. So, it really needs some dynamic parameters algorithm (X number of support units servicing X-1 peers and Y products in the period serviced based on some qty-measure/% square matrix allocation table) to spread their periodic cost to one unit of each product at the end. Preferably, somehow natively in SQL without using external .NET or other assembly references.
Some numeric example:
each of 3 support units A,B,C incurred $100, $200, $300 of expenses in the period and worked 50 man hrs each, respectively
A-unit serviced B-unit for 10 hrs and C-unit for 5 hrs, B-unit serviced A-unit for 5 hrs, C-unit serviced A-unit for 3 hrs and B-unit for 10 hrs
The rest of the support units' work time (A-unit 35 hrs: 30% for P1 and 70% for P2, B-unit 45 hrs: 35% for P1 and 65% for P2, C-unit 37 hrs for P2 for 100%) they spent servicing the output of two products (P1 and P2); this portion of their direct time/effort easily allocates to products - but due to reciprocal services to each other some share of support units' cost needs to be shifted to a respective product cost pool unequal to their direct time to product allocation (needs an adjusted mix coefficient for step 2 effects).
I could solve this in excel with iterating algorithm and use of VBA arrays:
(a) vector of period costs by each support unit (to finally reallocate to products and leave 0),
(b) 2dim array/matrix of coefficients of self-service between support units (based on man hrs - one to another),
(c) 2dim array/matrix of direct hrs service for each product by support units,
(d) minimal tolerable error of $1 (leftover of unallocated cost in a unit to stop iteration)
For just 2 or 3 elements (while still manually provable on paper) it is a feasible approach, but this becomes impossible to manually prove for a correct solution once I have 10-20+ support units and many products in a matrix; and I want to switch from excel and VBA to MS SQL server and t-sql for other reasons.
Since this business case as such is not new at all, I was hoping more experienced colleagues could throw an advise how to best solve this - I believed there must have been a solution to this task before (not in pure programming environment/external code).
I am thinking to combine CTE(recursive), table variables and aggregate window functions - but hesitate/struggle how to best/exactly put all puzzle elements together so it is truly scalable for my potentially growing unit/product matrix dimensions.
For my current level it's a little mind blowing, so I'd be grateful for an advice.
I have a project that consisted of transmitting data wirelessly from 15 tractors to a station, the maximum distance between tractor and station is 13 miles. I used a raspberry pi 3 to collect data from tractors. with some research I found that there is no wifi or GSM coverage so the only solution is to use RF communication using VHF. so is that possible with raspberry pi or I must add a modem? if yes, what is the criterion for choosing a modem? and please if you have any other information tell me?
and thank you for your time.
I had a similar issue but possibly a little more complex. I needed to cover a maximum distance of 22 kilometres and I wanted to monitor over 100 resources ranging from breeding stock to fences and gates etc. I too had no GSM access plus no direct line of sight access as the area is hilly and the breeders like the deep valleys. The solution I used was to make my own radio network using cheap radio repeaters. Everything was battery operated and was driven by the receivers powering up the transmitters. This means that the units consume only 40 micro amps on standby and when the transmitters transmit, in my case they consume around 100 to 200 milliamps.
In the house I have a little program that transmits a poll to the receivers every so often and waits for the units to reply. This gives me a big advantage because I can, via the repeater trail (as each repeater, the signal goes through, adds its code to the returning message) actually determine were my stock are.
Now for the big issue, how long do the batteries last? Well each unit has a 18650 battery. For the fence and gate controls this is charged by a small 5 volt solar panel and after 2 years running time I have not changed any of them. For the cattle units the length of time between charges depends solely on how often you poll the units (note each unit has its own code) with one exception (a bull who wants to roam and is a real escape artist) I only poll them once or twice a day and I swap the battery every two weeks.
The frequency I use is 433Mhz and the radio transmitters and receivers are very cheap ( less then 10 cents a pair if you by them in Australia) with a very small Attiny (I think) arduino per unit (around 30 cents each) and a length on wire (34.6cm long as an aerial) for the cattle and 69.2cm for the repeaters. Note these calculations are based on the frequency used i.e. 433Mhz.
As I had to install lots of the repeaters I contacted an organisation in China (sorry they no longer exist) and they created a tiny waterproof and rugged capsule that contained everything, while also improving on the design (range wise while reducing power) at a cost of $220 for 100 units not including batterys. I bought one lot as a test and now between myself and my neighbours we bought another 2000 units for only $2750.
In my case this was paid for in less then three months when during calving season I knew exactly were they were calving and was on site to assist. The first time I used it we saved a mother who was having a real issue.
To end this long message I am not an expert but I had an idea and hired people who were and the repeater approach certainly works over long distances and large areas (42 square kilometres).
Following on from the comments above, I'm not sure where you are located but spectrum around the 400mhz range is licensed in many countries so it would be worth checking exactly what you can use.
If this is your target then this is UHF rather than VHF so if you search for 'Raspberry PI UHF shield' or 'Raspberry PI UHF module' you will find some examples of cheap hardware you can add to your raspberry pi to support communication over these frequencies. Most of the results should include some software examples also.
There are also articles on using the pins on the PI to transmit directly by modulating the voltage them - this is almost certainly going to interfere with other communications so I doubt it would meet your needs.
I am using a startech capture card for capturing video from the source machine..I have encoded that video using matlab so every frame of that video will contain that marker...I run that video on the source computer(HDMI out) connected via HDMI to my computer(HDMI IN) once i capture the frame as bitmap(1920*1080) i re-size it to 1280*720 i send it for processing , the processing code checks every pixel for that marker.
The issue is my capture card is able to capture only at 1920*1080 where as the video is of 1280*720. Hence in order to retain the marker I am down scaling the frame captured to 1280*720 which in turn alters the entire pixel array I believe and hence I am not able to retain marker I fed in to the video.
In that capturing process the image is going through up-scaling which in turn changes the pixel values.
I am going through few research papers on Steganography but it hasn't helped so far. Is there any technique that could survive image resizing and I could retain pixel values.
Any suggestions or pointers will be really appreciated.
My advice is to start with searching for an alternative software that doesn't rescale, compress or otherwise modify any extracted frames before handing them to your control. It may save you many headaches and days worth of time. If you insist on implementing, or are forced to implement a steganography algorithm that survives resizing, keep on reading.
I can't provide a specific solution because there are many ways this can be (possibly) achieved and they are complex. However, I'll describe the ingredients a solution will most likely involve and your limitations with such an approach.
Resizing a cover image is considered an attack as an attempt to destroy the secret. Other such examples include lossy compression, noise, cropping, rotation and smoothing. Robust steganography is the medicine for that, but it isn't all powerful; it may be able to provide resistance to only specific types attacks and/or only small scale attacks at that. You need to find or design an algorithm that suits your needs.
For example, let's take a simple pixel lsb substitution algorithm. It modifies the lsb of a pixel to be the same as the bit you want to embed. Now consider an attack where someone randomly applies a pixel change of -1 25% of the time, 0 50% of the time and +1 25% of the time. Effectively, half of the time it will flip your embedded bit, but you don't know which ones are affected. This makes extraction impossible. However, you can alter your embedding algorithm to be resistant against this type of attack. You know the absolute value of the maximum change is 1. If you embed your secret bit, s, in the 3rd lsb, along with setting the last 2 lsbs to 01, you guarantee to survive the attack. More specifically, you get xxxxxs01 in binary for 8 bits.
Let's examine what we have sacrificed in order to survive such an attack. Assuming our embedding bit and the lsbs that can be modified all have uniform probabilities, the probability of changing the original pixel value with the simple algorithm is
change | probability
-------+------------
0 | 1/2
1 | 1/2
and with the more robust algorithm
change | probability
-------+------------
0 | 1/8
1 | 1/4
2 | 3/16
3 | 1/8
4 | 1/8
5 | 1/8
6 | 1/16
That's going to affect our PSNR quite a bit if we embed a lot of information. But we can do a bit better than that if we employ the optimal pixel adjustment method. This algorithm minimises the Euclidean distance between the original value and the modified one. In simpler terms, it minimises the absolute difference. For example, assume you have a pixel with binary value xxxx0111 and you want to embed a 0. This means you have to make the last 3 lsbs 001. With a naive substitution, you get xxxx0001, which has a distance of 6 from the original value. But xxx1001 has only 2.
Now, let's assume that the attack can induce a change of 0 33.3% of the time, 1 33.3% of the time and 2 33.3%. Of that last 33.3%, half the time it will be -2 and the other half it will be +2. The algorithm we described above can actually survive a +2 modification, but not a -2. So 16.6% of the time our embedded bit will be flipped. But now we introduce error correcting codes. If we apply such a code that has the potential to correct on average 1 error every 6 bits, we are capable of successfully extracting our secret despite the attack altering it.
Error correction generally works by adding some sort of redundancy. So even if part of our bit stream is destroyed, we can refer to that redundancy to retrieve the original information. Naturally, the more redundancy you add, the better the error correction rate, but you may have to double the redundancy just to improve the correction rate by a few percent (just arbitrary numbers here).
Let's appreciate here how much information you can hide in a 1280x720 (grayscale) image. 1 bit per pixel, for 8 bits per letter, for ~5 letters per word and you can hide 20k words. That's a respectable portion of an average novel. It's enough to hide your stellar Masters dissertation, which you even published, in your graduation photo. But with a 4 bit redundancy per 1 bit of actual information, you're only looking at hiding that boring essay you wrote once, which didn't even get the best mark in the class.
There are other ways you can embed your information. For example, specific methods in the frequency domain can be more resistant to pixel modifications. The downside of such methods are an increased complexity in coding the algorithm and reduced hiding capacity. That's because some frequency coefficients are resistant to changes but make embedding modifications easily detectable, then there are those that are fragile to changes but they are hard to detect and some lie in the middle of all of this. So you compromise and use only a fraction of the available coefficients. Popular frequency transforms used in steganography are the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT).
In summary, if you want a robust algorithm, the consistent themes that emerge are sacrificing capacity and applying stronger distortions to your cover medium. There have been quite a few studies done on robust steganography for watermarks. That's because you want your watermark to survive any attacks so you can prove ownership of the content and watermarks tend to be very small, e.g. a 64x64 binary image icon (that's only 4096 bits). Even then, some algorithms are robust enough to recover the watermark almost intact, say 70-90%, so that it's still comparable to the original watermark. In some case, this is considered good enough. You'd require an even more robust algorithm (bigger sacrifices) if you want a lossless retrieval of your secret data 100% of the time.
If you want such an algorithm, you want to comb the literature for one and test any possible candidates to see if they meet your needs. But don't expect anything that takes only 15 lines to code and 10 minutes of reading to understand. Here is a paper that looks like a good start: Mali et al. (2012). Robust and secured image-adaptive data hiding. Digital Signal Processing, 22(2), 314-323. Unfortunately, the paper is not open domain and you will either need a subscription, or academic access in order to read it. But then again, that's true for most of the papers out there. You said you've read some papers already and in previous questions you've stated you're working on a college project, so access for you may be likely.
For this specific paper, table 4 shows the results of resisting a resizing attack and section 4.4 discusses the results. They don't explicitly state 100% recovery, but only a faithful reproduction. Also notice that the attacks have been of the scale 5-20% resizing and that only allows for a few thousand embedding bits. Finally, the resizing method (nearest neighbour, cubic, etc) matters a lot in surviving the attack.
I have designed and implemented ChromaShift: https://www.facebook.com/ChromaShift/
If done right, steganography can resiliently (i.e. robustly) encode identifying information (e.g. downloader user id) in the image medium while keeping it essentially perceptually unmodified. Compared to watermarks, steganography is a subtler yet more powerful way of encoding information in images.
The information is dynamically multiplexed into the Cb Cr fabric of the JPEG by chroma-shifting pixels to a configurable small bump value. As the human eye is more sensitive to luminance changes than to chrominance changes, chroma-shifting is virtually imperceptible while providing a way to encode arbitrary information in the image. The ChromaShift engine does both watermarking and pure steganography. Both DRM subsystems are configurable via a rich set of of options.
The solution is developed in C, for the Linux platform, and uses SWIG to compile into a PHP loadable module. It can therefore be accessed by PHP scripts while providing the speed of a natively compiled program.
From the research I have done so far I learned that there the MIPS is highly dependent upon the application being run, or the language.
But can anyone give me their best guess for a 2.5 Ghz computer in MIPS? Or any other number of Ghz?
C++ if that helps.
MIPS stands for "Million Instructions Per Second", but that value becomes difficult to calculate for modern computers. Many processor architectures (such as x86 and x86_64, which make up most desktop and laptop computers) fall into the CISC category of processors. CISC architectures often contain instructions that perform several different tasks at once. One of the consequences of this is that some instructions take more clock cycles than other instructions. So even if you know your clock frequency (in this case 2.5 gigahertz), the number of instructions run per second depends mostly on which instructions a program uses. For this reason, MIPS has largely fallen out of use as a performance metric.
For some of my many benchmarks, identified in
http://www.roylongbottom.org.uk/
I produce an assembly code listing from which actual assembler instructions used can be calculated (Note that these are not actual micro instructions used by the RISC processors). The following includes %MIPS/MHz calculations based on these and other MIPS assumptions.
http://www.roylongbottom.org.uk/cpuspeed.htm
The results only apply for Intel CPUs. You will see that MIPS results depend on whether CPU, cache or RAM data is being used. For a modern CPU at 2500 MHz, likely MIPS are between 1250 and 9000 using CPU/L1 cache but much less accessing data in RAM. Then there are SSE SIMD integer instructions. Real integer MIPS for simple register based additions are in:
http://www.roylongbottom.org.uk/whatcpu%20results.htm#anchorC2D
Where my 2.4 GHz Core 2 CPU is shown to run at up to 17531 MIPS.
Roy
MIPS officially stands for Million Instructions Per Second but the Hacker's Dictionary defines it as Meaningless Indication of Processor Speed. This is because many companies use the theoretical maximum for marketing which is never achieved in real applications. E.g. current Intel processors can execute up to 4 instructions per cycle. Following this logic at 2.5 GHz it achieves 10,000 MIPS. In real applications, of course, this number is never achieved. Another problem, which slavik already mentions, is that instructions do different amounts of useful work. There are even NOPs, which–by definition–do nothing useful yet contribute to the MIPS rating.
To correct this people began using Dhrystone MIPS in the 1980s. Dhrystone is a synthetical benchmark (i.e. it is not based on a useful program) and one Dhrystone MIPS is defined relative to the benchmark performance of a VAX 11/780. This is only slightly less ridiculous than the definition above.
Today, performance is commonly measured by SPEC CPU benchmarks, which are based on real world programs. If you know these benchmarks and your own applications very well, you can make resonable predictions of performance without actually running your application on the CPU in question.
They key is to understand that performance will vary widely based on a number of characteristics. E.g. there used to be a program called The Many Faces of Go which essentially hard codes knowledge about the Board Game in many conditional if-clauses. The performance of this program is almost entirely determined by the branch predictor. Other programs use hughe amounts of memory that does not fit into any cache. The performance of these programs is determined by the bandwidth and/or latency of the main memory. Some applications may depend heavily on the throughput of floating point instructions while other applications never use any floating point instructions. You get the idea. An accurate prediction is impossible without knowing the application.
Having said all that, an average number would be around 2 instructions per cycle and 5,000 MIPS # 2.5 GHz. However, real numbers can be easily ten or even a hundred times lower.