For my project, I would need to develop a data logger able to read data (images) from a USB camera and store them on a micro SD card. The power consumption of this datalogger should be very low. I found some microcontroller with the features that I need. For example http://www.ti.com/tool/msp-exp430f5529 but, during my researches, I've also found some DSPs.
I know that DSPs are more for signal processing, but I've read that nowadays the difference between a DSP and a microcontroller, in terms of performance, is very small.
My question is: can a DSP (such as http://www.ti.com/product/TMS320C5535/datasheet) be used for this purpose? Or a standard Micro controller would be better?
Thank you.
A DSP will do for sure the work, consider that DSPs are intended to be implemented on Signal Processing applications this menas asweel in the background that DSP are able to perform complex floating point operations at a very low cycle rate, 'modulo' or 'wrapping' addressing operations, 'buffer start', 'buffer end' register, reverse-carry flag etc etc
At the end your question means for me something like you are asking suggestions to buy either an Iphone-8 or a Nokia 3210 only to send sms :) missing the high purpose and performance of the device....
A very "normal" μController with an 10 bits ADC could do the job too... both can be implementened in a very low current consumption and are very straight forward to be flashed
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I need to calculate DRAM access latency using given data size to be transfered between DRAM-SRAM
The data is seperated to "load size" and "store size" and "number of iteration of load and store" is given.
I think the features I need to consider are many like first DRAM access latency, transfer one word latency, address load latency etc..
Is there some popular equation to get this by given information?
Thank you in advance.
Your question has many parts, I think I can help better if I knew the ultimate goal? If it's simply to measure access latency:
If you are using an x86 processor maybe the Intel Memory Latency Checker will help
Intel® Memory Latency Checker (Intel® MLC) is a tool used to measure memory latencies and b/w, and how they change with increasing load on the system. It also provides several options for more fine-grained investigation where b/w and latencies from a specific set of cores to caches or memory can be measured as well.
If not x86, I think the Gem5 Simulator has what you are looking for, here is the main page but more specifically, for your needs, I think this config for Gem5 will be the most helpful.
Now regarding a popular equation, the best I could find is this Carnegie Melon paper that goes over my head: https://users.ece.cmu.edu/~omutlu/pub/chargecache_low-latency-dram_hpca16.pdf However, it looks like your main "features" as you put it revolve around cores and memory channels. The equation from the paper:
Storagebits = C ∗MC ∗Entries∗(EntrySizebits +LRUbits)
Is used to create a cache that will ultimately (the goal of ChargeCache) reduce access latency in DRAM. I'm sure this isn't the equation you are looking for but just a piece of the puzzle. The LRUbits relate to the cache this mechanism (in the memory controller, no DRAM modification necessary) creates.
EntrySizebits is determined by this equation EntrySizebits = log2(R)+log2(B)+log2(Ro)+1 and
R, B, and Ro are the number of ranks, banks, and rows in DRAM, respectively
I was surprised to learn highly charged rows (recently accessed) will have a significantly lower access latency.
If this goes over your head as well, maybe this 2007 paper by Ulrich Drepper titled What Every Programmer Should Know About Memory will help you find the elements you need for your equation. I'm still working through this paper myself, and there is some dated references but those depend on what cpu you're working with. Hope this helps, I look forward to being corrected on any of this, as I'm new to the topic.
I am a grad student and in our lab, we have a Dell Precision T3500 (http://www.dell.com/us/business/p/precision-t3500/pd). We use it primarily for image processing research and we need to use OpenCV 2.4.7's "ocl" i.e., OpenCL bindings for parallelizing up our work for some publications.
I looked at the workstation's specs and it specifies that we can get a NVIDIA Quadro 5000 or an AMD FirePro V7900 (the best of both manufacturers for this workstation).
This is where I am confused. Most of the reviews compare performance for CAD/CAM, MAYA and other software. But we will be writing our own code using OpenCV. Can anyone help me out in choosing the best of these two GPUs? Or is there anyway I can get a better GPU by upgrading the power supply?
We would greatly appreciate all the advice we can get at this stage!
Thank you very much.
If you are using OpenCL I agree with DarkZeros. You probably should buy AMD HW. Nvidia supports OpenCL only grudgingly as they want everyone to use CUDA.
Both of the cards you showed seem to be rather similar. Theoretical maximum at around 1TFlops. However both of them are rather old and very expensive. If you are not bound by any purchasing agreement I really recommend you buy a consumer card. The specs in dell.com only mean that if you purchase the computer from them you can select a GPU for it. It does not limit what you can do afterwards.
Depending on the chassis you could change your power supply. That would mean you could purchase something like this http://www.amazon.com/XFX-RADEON-1000MHz-Graphics-R9290XENFC/dp/B00G2OTRMA . It has double the memory of either of those professional cards and over 5x the theoretical processing power.
To be fair if you have the money to spend GTX Titan is still an excellent choice. It is about as fast as that AMD card and you can use CUDA with it if you need, considering how common CUDA is in scientific computing it might be wise to go with that.
However if you cannot switch your power supply, if it's non standard size or whatnot, then you are more limited. In that case you should search for pretty much the heftiest card that can run on 150W. Even those have perhaps double the performance of the cards the computer was originally available with.
I'm working on robot vision system and its main purpose is to detect objects, i want to choose one of these libraries (CImg , OpenCV) and I have knowledge about both of them.
The robot I'm using has Linux , 1GHz CPU and 1G ram and I'm using C++ the size of image is 320p.
I want to have a real-time image processing near 20 out of 25 frames per seconds.
In your opinion which library is more powerful l although I have tested both and they have the same process time, open cv is slightly better and I think that's because I use pointers with open cv codes.
Please share your idea and your reason.
thanks.
I think you can possibly get best performance when you integrated - OpenCV with IPP.
See this reference, http://software.intel.com/en-us/articles/intel-integrated-performance-primitives-intel-ipp-open-source-computer-vision-library-opencv-faq/
Here is another reference http://experienceopencv.blogspot.com/2011/07/speed-up-with-intel-integrated.html
Further, if you freeze the algorithm that works perfectly, usually you can isolate your algorithm and work your way towards doing serious optimization (such as memory optimization, porting to assembly etc.) which might not be ready to use.
It really depends on what you want to do (what kind of objects you want to detect, accuracy, what algorithm you are using etc..) and how much time you have got. If it is for generic computer vision/image processing, I would stick with OpenCV. As Dipan said, do consider further optimization. In my experience with optimization for Computer Vision, the bottleneck usually is in memory interconnect bandwidth (or memory itself) and so you might have to trade in cycles (computation) to save on communication. Do understand the algorithm really well to further optimize the algorithm (which at times can give huge improvements as compared to compilers).
I read that several DSP cards that process audio, can calculate very fast Fourier Transforms and some other functions involved in Sound processing and others. There are some scientific problems (not many), like Quantum mechanics, that involver Fourier Transform calculations. I wonder if DSP could be used to accelerate calculations in this fashion, like GPUs do in some other cases, and if you know succcessful examples.
Thanks
Any linear operations are easier and faster to do on DSP chips. Their architecture allows you to perform a linear operation (take two numbers, multiply each of them by a constant and add the results) in a single clock cycle. This is one of the reasons FFT can be calculated so quickly on a DSP chip. This is also a reason many other linear operations can be accelerated with their use. I guess I have three main points to make concerning performance and code optimization for such processors.
1) Perhaps less relevant, but I'd like to mention it nonetheless. In order to take full advantage of DSP processor's architecture, you have to code in Assembly. I'm pretty sure that regular C code will not be fully optimized by the compiler to do what you want. You literally have to specify each register, etc. It does pay off, however. The same way, you are able to make use of circular buffers and other DSP-specific things. Circular buffers are also very useful for calculating the FFT and FFT-based (circular) convolution.
2) FFT can be found in solutions to many problems, such as heat flow (Fourier himself actually came up with the solution back in the 1800s), analysis of mechanical oscillations (or any linear oscillators for that matter, including oscillators in quantum physics), analysis of brain waves (EEG), seismic activity, planetary motion and many other things. Any mathematical problem that involves convolution can be easily solved via the Fourier transform, analog or discrete.
3) For some of the applications listed above, including audio processing, other transforms other than FFT are constantly being invented, discovered, and applied to processing, such as Mel-Cepstrum (e.g. MPEG codecs), wavelet transform (e.g. JPEG2000 codecs), discrete cosine transform (e.g. JPEG codecs) and many others. In quantum physics, however, the Fourier Transform is inherent in the equation of angular momentum. It arises naturally, not just for the purposes of analysis or easy of calculations. For this reason, I would not necessarily put the reasons to use Fourier Transform in audio processing and quantum mechanics into the same category. For signal processing, it's a tool; for quantum physics, it's in the nature of the phenomenon.
Before GPUs and SIMD instruction sets in mainstream CPUs this was the only way to get performance for some applications. In the late 20th Century I worked for a company that made PCI cards to place extra processors in a PCI slot. Some of these were DSP cards using a TI C64x DSP, others were PowerPC cards to provide Altivec. The processor on the cards would typically have no operating system to give more predicatable real-time scheduling than the host. Customers would buy an industrial PC with a large PCI backplace, and attach multiple cards. We would also make cards in form factors such as PMC, CompactPCI, and VME for more rugged environments.
People would develop code to run on these cards, and host applications which communicated with the add-in card over the PCI bus. These weren't easy platforms to develop for, and the modern libraries for GPU computing are much easier.
Nowadays this is much less common. The price/performance ratio is so much better for general purpose CPUs and GPUs, and DSPs for scientific computing are vanishing. Current DSP manufacturers tend to target lower power embedded applications or cost sensitive high volume devices like digital cameras. Compare GPUFFTW with these Analog Devices benchmarks. The DSP peaks at 3.2GFlops, and the Nvidia 8800 reachs 29GFlops.
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I have a robot project and it needs to process images coming from a camera. But I am looking for a microcontroller to have image processing on its own, free of any computer or laptop. Does such a microcontroller exist? What is it? And how is it done?
I think you're taking the wrong approach to your question. At its core, a microcontroller is pretty much just a computation engine with some variety of peripheral modules. The features that vary generally are meant to fulfill an application where a certain performance metric is needed. So in that respect any generic microcontroller will suffice assuming it meets your performance criteria. I think what you should be asking is:
What computations do you want to perform? All the major controller vendors offer some sort of graphics processing libraries for use on their chips. You can download them and look through their interfaces to see if they offer the operations that you need. If you can't find a library that does everything you need then you might have to roll your own graphics library.
Memory constraints? How big will the images be? Will you process an image in its entirety or will you process chunks of an image at a time? This will affect how much memory you'll require your controller to have.
Timing constraints? Are there certain deadlines that need to be met like the robot needing results within a certain period of time after the image is taken? This will affect how fast your processor will need to be or whether a potential controller needs dedicated computation hardware like barrel shifters or multiply-add units to speed the computations along.
What else needs to be controlled? If the controller also needs to control the robot then you need to address what sort of peripherals the chip will need to interface with the robot. If another chip is controlling the robot then you need to address what sort of communications bus is available to interface with the other chip.
Answer these questions first and then you can go and look at controller vendors and figure out which chip suits your needs best. I work mostly with Microchip PIC's these days so I'd suggest the dsPIC33 line from that family as a starting point. The family is built for DSP applications as it's peripheral library includes some image processing stuff and it has the aforementioned barrel-shifters and multiply-add hardware units intended for applications like filters and the like.
It is impossible to answer your question without knowing what image processing it is you need to do, and how fast. For a robot I presume this is real-time processing where a result needs to be available perhaps at the frame rate?
Often a more efficient solution for image processing tasks is to use an FPGA rather than a microprocessor since it allows massive parallelisation and pipe-lining, and implements algorithms directly in logic hardware rather than sequential software instructions so that very sophisticated image processing can be achieved at relatively low clock rates, an FPGA running at just 50 MHz can easily outperform a desktop class processor when performing specialised tasks. Some tasks would be impossible to achieve in any other way.
Also worth consideration is a DSP, this will not have the performance of an FPGA but is easier to use perhaps and more flexible, and is designed to move data rapidly and to execute instructions efficiently, often including a level of instruction level parallelisation.
If you want a conventional microprocessor, then you have to throw clock cycles at the problem (brute force), then an ARM 11, Renesas SH-4, or even an Intel Atom may be suitable. For lower end tasks an ARM Cortex-M4, which includes a DSP engine and optionally floating point hardware may be suited.
The CMUcam3 is the combination of a small camera and an ARM-based microcontroller that is freely programmable. I've programmed image processing code on it before. One caveat, however, is that it only has 64 KB of RAM, so any processing you want to do must be done scanline-by-scanline.
Color object tracking and similar simple image processing can be done with AVRcam. For more intensive processing I would use OpenCV on some ARM Linux board.