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I have completed my degree in Computer Engineering. We had some basic electronics courses in Digital Signal Processing, Information Theory, etc. but my primary field is Programming.
However, I was looking to get into Embedded Systems Programming, and I have NO knowledge of how it is done. However, I am very keen on going into this field.
My questions :
What are the languages used to program embedded systems?
Will I be able to learn without having any basics in electronics?
Any other prerequisites that I should know?
Without a doubt, experience or at least a significant understanding of digital electronics and low level computer engineering is required. You'll need to be able to read device datasheets and understand them. Scopes, multimeters, logic analyzers, etc... are tools of the trade.
C is used mostly, but higher level languages are sneaking in slowly.
Getting started in Embedded Systems is a complex task in itself, because it is a very vast field with numerous options in hardware and software.
What are the languages used to program embedded system programs?
Assembly Language, C, C++, Python, C# and others.
Will I be able to learn without having any basics in electronics?
Learning embedded systems without the basic knowledge of electronics would not be a good idea. Embedded systems is a mix of hardware and software. You can follow the approach of learning-by-doing instead of going through the lengthy and detailed text books.You can refer to this blog
to learn embedded systems by doing practicals, step by step. It will help you to get started from the scratch.
Any other prerequisites that I should know?
Basic electronics, digital electronics, knowledge of microcontrollers and C programming. Since you are from computer science background you would need a development board of any 8-bit microcontroller (students of EE and ECE have enough knowledge and background to build it on breadboard or pcb) to get started. (Don't prefer simulators in the start, you might get your concepts wrong!).
You have to accept constraints and be able to work with them:
CPU speed
scarce memory
lack of networking facilities
custom compilers and OSes
custom motherboards and drivers
debugging with a logic analyzer
weird coding and testing practices
...
The reward is a deep understanding on what is going on.
VHDL, Verilog, and FPGA's are serious players in this arena as well. With a good background in CS, plenty of commitment, and maybe some MIT OpenCourseware you'll be able to pull off something good. A good knowledge of cpu architectures and some ASM will go a long way too.
I went into that field with no knowledge of how it was done as a fresh graduate and quit after 1,5 years. So, what I say may be a little bit rusty, and definitely not comprehensive.
The language we were using was C. But at that time, the disc space was 4MB and memory was 8MB on the devices we were developing for, and I know that C was used due to its libraries' tiny footprint. Apparently, performance was a criterion as well.
As to basic electronics, for an entry level position almost none is necessary. You will gain the required information and experience with time.
Not prerequisites, but having experience in the operating system internals and system development is definitely a plus.
Embedded systems are generally programmed in C, although there are systems at the ends of the range which use assembler when code space or timing is really tight (or there is no decent C compiler available), and at the other end, C++ up to .NET compact. It depends on what you mean by embedded systems, they go from really small microcontrollers with a few hundred bytes of RAM and program space, up to the smartphone type of device running a full multitasking operating system and user interface.
You'll get further in the higher end of this range without a background in electronics, because its less tied to the hardware and more similar to desktop development. As you go down the range of applications, a knowledge of electronics, analogue and digital, and power supplies, noise issues, compliance issues, heat issues and others all combine to make a really challenging design environment.
Start by reading some of the links here and embedded.com
The one thing that I have not seen mentioned in the answers so far is that up until now you have probably done most of your coding in the context of an operating system. In many (perhaps most?) cases, with firmware as opposed to software, you will not have the convenience and benefits of coding on top of an operating system. This is why so many of the other answers indicated that a good knowledge of electronics was critical.
As others mentioned, embedded can mean many things. In my world (Aerospace and Defense), we work with real-time operating systems (VxWorks and Integrity are the biggest players) and occasionally Linux. We program in C primarily, although C++ is also used as well if the project has decided to use Object Oriented Programming and Modeling.
So, as for the Pre-Reqs, C for sure. You really need to learn all about C, including BIT wise operations, dealing with hex values, pointers, all the low level stuff. Assembly as well, but I only use it for debugging the hardest stuff nowadays. You need to know enough to read and understand.
I think An Embedded Software Primer is a great start to change your thinking towards embedded. Handling interrupts, real-time issues, etc...
As Mickey mentioned, sometimes you don't even have an OS. In these cases, you usually write your own task manager of some sort, but that usually wouldn't be something for the newbie to start with. Good luck.
Languages: C, Assembler, Processing, Basic and a whole variety of others, it depends on what platform you're using as to what's available.
You might get more specific information if you ask the same question at ChipHacker or Electronics Exchange which are both stack exchange style sites (like this is) but geared to electronics and "physical computing".
You'll want to get pretty comfortable with C and build a solid understanding of assembly. In systems / embedded, usually you're working with small amounts of memory and slower processors, so you need to understand how to use limited resources wisely.
If you're getting into this as a hobby, pick up a gumstix board or an arduino, these dev boards will give you hundreds of hours of learning and fun.
If you're trying to make a career of this, find a job where the projects sound interesting and get your hands dirty. Take every task that comes your way and ask yourself how you can do better and learn from this task.
Either way, have fun and happy coding!
Learn C. Learn to apply C to all problems. Other languages can wait. Eventually assembly will help and no programmer should be without the use a scripting language.
Depending on what embedded targets you use there could be very little difference between a PC and your target. With little electronics background this would be your easiest entry.
Small processors will give you the the steepest learning curve but you will learn the most about embedded programming. However with no electronic background this can present extra problems you might not have the skills to solve yet.
Eventually you will have to learn electronics if you want to make further progress beyond the basics.
What are the pros and cons in choosing PS3 as a platform for scientific computing in detriment of GPU's? Is It the better choice ?
Stick with a PC, you will have a far easier life at the end of the day. I also wouldn't be surprised if you get more horsepower out of GPU's.
p.s., from what I know dispatching work to the cells is not an enjoyable task :D
I'd go for GPU, for three reasons:
(a) GPU code can be developed, tested, and run on pretty much any PC you may want to use, with the only dependency being a $150 video card, whereas CELL/PS3 is a much more custom development environment and won't run natively on your laptop, etc.;
(b) I'm willing to bet a lot that GPUs and Cuda will be alive and well in 5 years, but I wouldn't put money on PS3 being around that long -- what are you going to do if PS4 has a totally different architecture and CELL effectively dies?
(c) There's a more vibrant research and development community around GPU than there is around PS3/Cell (outside of strict game development), so you're likely to be in more good company, have example code and tools to work with, etc.
There is no broad "better" choice, it is all dependent on the situation and what you're doing. Probably the biggest PRO to a PS3 is they're cheap by comparison. A computer can more easily scale bigger though (for a price) when looking into things like CUDA.
CUDA is pretty slick. I was shown a presentation recently demonstrating how easy it is to get at the power of the GPU's many cores using a C++ based syntax. If I was starting a parallel computing project now, I would probably take the PC/GPU-based route.
A major objection to the PS3 (which is already quite a wacky choice unless you're under some pretty extreme price/performance constraints) has to be that Sony are dropping support for installation of other OS. In future, PS3s without the disabling firmware update may become harder and harder to get hold of.
I want to get into multi core programming (not language specific) and wondered what hardware could be recommended for exploring this field.
My aim is to upgrade my existing desktop.
If at all possible, I would suggest getting a dual-socket machine, preferably with quad-core chips. You can certainly get a single-socket machine, but dual-socket would let you start seeing some of the effects of NUMA memory that are going to be exacerbated as the core counts get higher and higher.
Why do you care? There are two huge problems facing multi-core developers right now:
The programming model Parallel programming is hard, and there is (currently) no getting around this. A quad-core system will let you start playing around with real concurrency and all of the popular paradigms (threads, UPC, MPI, OpenMP, etc).
Memory Whenever you start having multiple threads, there is going to be contention for resources, and the memory wall is growing larger and larger. A recent article at arstechnica outlines some (very preliminary) research at Sandia that shows just how bad this might become if current trends continue. Multicore machines are going to have to keep everything fed, and this will require that people be intimately familiar with their memory system. Dual-socket adds NUMA to the mix (at least on AMD machines), which should get you started down this difficult road.
If you're interested in more info on performance inconsistencies with multi-socket machines, you might also check out this technical report on the subject.
Also, others have suggested getting a system with a CUDA-capable GPU, which I think is also a great way to get into multithreaded programming. It's lower level than the stuff I mentioned above, but throw one of those on your machine if you can. The new Portland Group compilers have provisional support for optimizing loops with CUDA, so you could play around with your GPU even if you don't want to learn CUDA yourself.
Quad-core, because it'll permit you to do problems where the number of concurrent processes is > 2, which often non-trivializes problems.
I would also, for sheer geek squee, pick up a nice NVidia card and use the CUDA API. If you have the bucks, there's a stand-alone CUDA workstation that plugs into your main computer via a cable and an expansion slot.
It depends what you want to do.
If you want to learn the basics of multithreaded programming, then you can do that on your existing single-core PC. (If you have 2 threads, then the OS will switch between them on a single-core PC. Then when you move to a dual-core PC they should automatically run in parallel on separate cores, for a 2x speedup). This has the advantage of being free! The disadvantages are that you won't see a speedup (in fact a parallel implementation is probably slightly slower due to overheads), and that buggy code has a slightly higher chance of working.
However, although you can learn multithreaded programming on a single-core box, a dual-core (or even HyperThreading) CPU would be a great help.
If you want to really stress-test the code you're writing, then as "blue tuxedo" says, you should go for as many cores as you can easily afford, and if possible get hyperthreading too.
If you want to learn about algorithms for running on graphics cards - which is a very different area to x86 multicore - then get CUDA and buy a normal nVidia graphics card that supports it.
I'd recommend at least a quad-core processor.
You could try tinkering with CUDA. It's free, not that hard to use and will run on any recent NVIDIA card.
Alternatively, you could get a PlayStation 3 and the Linux SDK and work out how to program a Cell processor. Note that the next cheapest option for Cell BE development is an order of magnitude more expensive than a PS3.
Finally, any modern motherboard that will take a Core Quad or quad-core Opteron (get a good one from Asus or some other reputable manufacturer) will let you experiment with a multi-core PC system for a reasonable sum of money.
The difficult thing with multithreaded/core programming is that it opens a whole new can of worms. The bugs you'll be faced with are usually not the one you're used to. Race conditions can remain dormant for ages until they bite and your mainstream language compiler won't assist you in any way. You'll get random data and/or crashes that only happen once a day/week/month/year, usually under the most mysterious conditions...
One things remains true fortunately : the higher the concurrency exhibited by a computer, the more race conditions you'll unveil.
So if you're serious about multithreaded/core programming, then go for as many cpu cores as possible. Keep in mind that neither hyperthreading nor SMT allow for the level of concurrency that multiple cores provide.
I would agree that, depending on what you ultimately want to do, you can probably get by with just your current single-core system. Multi-core programming is basically multi-threaded programming, and you can certainly do that on a single-core chip.
When I was a student, one of our projects was to build a thread-safe implementation the malloc library for C. Even on a single core processor, that was more than enough to cure me of my desire to get into multi-threaded programming. I would try something small like that before you start thinking about spending lots of money.
I agree with the others where I would upgrade to a quad-core processor. I am also a BIG FAN of ASUS Motherboards (the P5Q Pro is excellent for Core2Quad and Core2Duo processors)!
The draw for multi-core programming is that you have more resources to get things done faster. If you are serious about multi-core programming, then I would absolutely get a quad-core processor. I don't believe that you should get the new i7 architecture from Intel to take advantage of multi-core processing because anything written to take advantage of the Core2Duo or Core2Quad will just run better on the newer architecture.
If you are going to dabble in multi-core programming, then I would get a good Core2Duo processor. Remember, it's not just how many cores you have, but also how FAST the cores are to process the jobs. My Core2Duo running at 4GHz routinely completes jobs faster than my Core2Quad running at 2.4GHz even with a multi-core program.
Let me know if this helps!
JFV
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I am developing a product with heavy 3D graphics computations, to a large extent closest point and range searches. Some hardware optimization would be useful. While I know little about this, my boss (who has no software experience) advocates FPGA (because it can be tailored), while our junior developer advocates GPGPU with CUDA, because its cheap, hot and open. While I feel I lack judgement in this question, I believe CUDA is the way to go also because I am worried about flexibility, our product is still under strong development.
So, rephrasing the question, are there any reasons to go for FPGA at all? Or is there a third option?
I investigated the same question a while back. After chatting to people who have worked on FPGAs, this is what I get:
FPGAs are great for realtime systems, where even 1ms of delay might be too long. This does not apply in your case;
FPGAs can be very fast, espeically for well-defined digital signal processing usages (e.g. radar data) but the good ones are much more expensive and specialised than even professional GPGPUs;
FPGAs are quite cumbersome to programme. Since there is a hardware configuration component to compiling, it could take hours. It seems to be more suited to electronic engineers (who are generally the ones who work on FPGAs) than software developers.
If you can make CUDA work for you, it's probably the best option at the moment. It will certainly be more flexible than a FPGA.
Other options include Brook from ATI, but until something big happens, it is simply not as well adopted as CUDA. After that, there's still all the traditional HPC options (clusters of x86/PowerPC/Cell), but they are all quite expensive.
Hope that helps.
We did some comparison between FPGA and CUDA. One thing where CUDA shines if you can realy formulate your problem in a SIMD fashion AND can access the memory coalesced. If the memory accesses are not coalesced(1) or if you have different control flow in different threads the GPU can lose drastically its performance and the FPGA can outperform it. Another thing is when your operation is realtive small, but you have a huge amount of it. But you cant (e.g. due to synchronisation) no start it in a loop in one kernel, then your invocation times for the GPU kernel exceeds the computation time.
Also the power of the FPGA could be better (depends on your application scenarion, ie. the GPU is only cheaper (in terms of Watts/Flop) when its computing all the time).
Offcourse the FPGA has also some drawbacks: IO can be one (we had here an application were we needed 70 GB/s, no problem for GPU, but to get this amount of data into a FPGA you need for conventional design more pins than available). Another drawback is the time and money. A FPGA is much more expensive than the best GPU and the development times are very high.
(1) Simultanously accesses from different thread to memory have to be to sequential addresses. This is sometimes really hard to achieve.
I would go with CUDA.
I work in image processing and have been trying hardware add-ons for years. First we had i860, then Transputer, then DSP, then the FPGA and direct-compiliation-to-hardware.
What innevitably happened was that by the time the hardware boards were really debugged and reliable and the code had been ported to them - regular CPUs had advanced to beat them, or the hosting machine architecture changed and we couldn't use the old boards, or the makers of the board went bust.
By sticking to something like CUDA you aren't tied to one small specialist maker of FPGA boards. The performence of GPUs is improving faster then CPUs and is funded by the gamers. It's a mainstream technology and so will probably merge with multi-core CPUs in the future and so protect your investment.
FPGAs
What you need:
Learn VHDL/Verilog (and trust me you don't want to)
Buy hw for testing, licences for synthesis tools
If you already have infrastructure and you need to develop only your core
Develop design ( and it can take years )
If you don't:
DMA, hw driver, ultra expensive synthesis tools
tons of knowledge about buses, memory mapping, hw synthesis
build the hw, buy the ip cores
Develop design
Not mentioning of board developement
For example average FPGA pcie card with chip Xilinx ZynqUS+ costs more than 3000$
FPGA cloud is also costly 2$/h+
Result:
This is something which requires resources of running company at least.
GPGPU (CUDA/OpenCL)
You already have hw to test on.
Compare to FPGA stuff:
Everything is well documented .
Everything is cheap
Everything works
Everything is well integrated to programming languages
There is GPU cloud as well.
Result:
You need to just download sdk and you can start.
This is an old thread started in 2008, but it would be good to recount what happened to FPGA programming since then:
1. C to gates in FPGA is the mainstream development for many companies with HUGE time saving vs. Verilog/SystemVerilog HDL. In C to gates System level design is the hard part.
2. OpenCL on FPGA is there for 4+ years including floating point and "cloud" deployment by Microsoft (Asure) and Amazon F1 (Ryft API). With OpenCL system design is relatively easy because of very well defined memory model and API between host and compute devices.
Software folks just need to learn a bit about FPGA architecture to be able to do things that are NOT EVEN POSSIBLE with GPUs and CPUs for the reasons of both being fixed silicon and not having broadband (100Gb+) interfaces to the outside world. Scaling down chip geometry is no longer possible, nor extracting more heat from the single chip package without melting it, so this looks like the end of the road for single package chips. My thesis here is that the future belongs to parallel programming of multi-chip systems, and FPGAs have a great chance to be ahead of the game. Check out http://isfpga.org/ if you have concerns about performance, etc.
FPGA-based solution is likely to be way more expensive than CUDA.
Obviously this is a complex question. The question might also include the cell processor.
And there is probably not a single answer which is correct for other related questions.
In my experience, any implementation done in abstract fashion, i.e. compiled high level language vs. machine level implementation, will inevitably have a performance cost, esp in a complex algorithm implementation. This is true of both FPGA's and processors of any type. An FPGA designed specifically to implement a complex algorithm will perform better than an FPGA whose processing elements are generic, allowing it a degree of programmability from input control registers, data i/o etc.
Another general example where an FPGA can be much higher performance is in cascaded processes where on process outputs become the inputs to another and they cannot be done concurrently. Cascading processes in an FPGA is simple, and can dramatically lower memory I/O requirements while processor memory will be used to effectively cascade two or more processes where there are data dependencies.
The same can be said of a GPU and CPU. Algorithms implemented in C executing on a CPU developed without regard to the inherent performance characteristics of the cache memory or main memory system will not perform as well as one implemented which does. Granted, not considering these performance characteristics simplifies implementation. But at a performance cost.
Having no direct experience with a GPU, but knowing its inherent memory system performance issues, it too will be subjected to performance issues.
CUDA has a fairly substantial code base of examples and a SDK, including a BLAS back-end. Try to find some examples similar to what you are doing, perhaps also looking at the GPU Gems series of books, to gauge how well CUDA will fit your applications. I'd say from a logistic point of view, CUDA is easier to work with and much, much cheaper than any professional FPGA development toolkit.
At one point I did look into CUDA for claim reserve simulation modelling. There is quite a good series of lectures linked off the web-site for learning. On Windows, you need to make sure CUDA is running on a card with no displays as the graphics subsystem has a watchdog timer that will nuke any process running for more than 5 seconds. This does not occur on Linux.
Any mahcine with two PCI-e x16 slots should support this. I used a HP XW9300, which you can pick up off ebay quite cheaply. If you do, make sure it has two CPU's (not one dual-core CPU) as the PCI-e slots live on separate Hypertransport buses and you need two CPU's in the machine to have both buses active.
What are you deploying on? Who is your customer? Without even know the answers to these questions, I would not use an FPGA unless you are building a real-time system and have electrical/computer engineers on your team that have knowledge of hardware description languages such as VHDL and Verilog. There's a lot to it and it takes a different frame of mind than conventional programming.
I'm a CUDA developer with very littel experience with FPGA:s, however I've been trying to find comparisons between the two.
What I've concluded so far:
The GPU has by far higher ( accessible ) peak performance
It has a more favorable FLOP/watt ratio.
It is cheaper
It is developing faster (quite soon you will literally have a "real" TFLOP available).
It is easier to program ( read article on this not personal opinion)
Note that I'm saying real/accessible to distinguish from the numbers you will see in a GPGPU commercial.
BUT the gpu is not more favorable when you need to do random accesses to data. This will hopefully change with the new Nvidia Fermi architecture which has an optional l1/l2 cache.
my 2 cents
Others have given good answers, just wanted to add a different perspective. Here is my survey paper published in ACM Computing Surveys 2015 (its permalink is here), which compares GPU with FPGA and CPU on energy efficiency metric. Most papers report: FPGA is more energy efficient than GPU, which, in turn, is more energy efficient than CPU. Since power budgets are fixed (depending on cooling capability), energy efficiency of FPGA means one can do more computations within same power budget with FPGA, and thus get better performance with FPGA than with GPU. Of course, also account for FPGA limitations, as mentioned by others.
FPGA will not be favoured by those with a software bias as they need to learn an HDL or at least understand systemC.
For those with a hardware bias FPGA will be the first option considered.
In reality a firm grasp of both is required & then an objective decision can be made.
OpenCL is designed to run on both FPGA & GPU, even CUDA can be ported to FPGA.
FPGA & GPU accelerators can be used together
So it's not a case of what is better one or the other. There is also the debate about CUDA vs OpenCL
Again unless you have optimized & benchmarked both to your specific application you can not know with 100% certainty.
Many will simply go with CUDA because of its commercial nature & resources. Others will go with openCL because of its versatility.
FPGAs are more parallel than GPUs, by three orders of magnitude. While good GPU features thousands of cores, FPGA may have millions of programmable gates.
While CUDA cores must do highly similar computations to be productive, FPGA cells are truly independent from each other.
FPGA can be very fast with some groups of tasks and are often used where a millisecond is already seen as a long duration.
GPU core is way more powerful than FPGA cell, and much easier to program. It is a core, can divide and multiply no problem when FPGA cell is only capable of rather simple boolean logic.
As GPU core is a core, it is efficient to program it in C++. Even it it is also possible to program FPGA in C++, it is inefficient (just "productive"). Specialized languages like VDHL or Verilog must be used - they are difficult and challenging to master.
Most of the true and tried instincts of a software engineer are useless with FPGA. You want a for loop with these gates? Which galaxy are you from? You need to change into the mindset of electronics engineer to understand this world.
at latest GTC'13 many HPC people agreed that CUDA is here to stay. FGPA's are cumbersome, CUDA is getting quite more mature supporting Python/C/C++/ARM.. either way, that was a dated question
Programming a GPU in CUDA is definitely easier. If you don't have any experience with programming FPGAs in HDL it will almost surely be too much of a challenge for you, but you can still program them with OpenCL which is kinda similar to CUDA. However, it is harder to implement and probably a lot more expensive than programming GPUs.
Which one is Faster?
GPU runs faster, but FPGA can be more efficient.
GPU has the potential of running at a speed higher than FPGA can ever reach. But only for algorithms that are specially suited for that. If the algorithm is not optimal, the GPU will loose a lot of performance.
FPGA on the other hand runs much slower, but you can implement problem-specific hardware that will be very efficient and get stuff done in less time.
It's kinda like eating your soup with a fork very fast vs. eating it with a spoon more slowly.
Both devices base their performance on parallelization, but each in a slightly different way. If the algorithm can be granulated into a lot of pieces that execute the same operations (keyword: SIMD), the GPU will be faster. If the algorithm can be implemented as a long pipeline, the FPGA will be faster. Also, if you want to use floating point, FPGA will not be very happy with it :)
I have dedicated my whole master's thesis to this topic.
Algorithm Acceleration on FPGA with OpenCL
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Given my background as a generalist, I can cover much of the area from analog electronics to writing simple applications that interface to a RDBMS backend.
I currently work in a company that develops hardware to solve industry-specific problems. We have an experienced programmer that have written business apps, video games, and a whole bunch of other stuff for PC's. But when I talk to him about doing low-level programming, he simultaneously express interest and also doubt/uncertainty about joining the project.
Even when talking about PC's, he seems to be more comfortable operating at the language level than the lower-level stuff (instruction sets, ISR's). Still, he's a smart guy, and I think he'd enjoy the work once he is over the initial learning hump. But maybe that's my own enthusiasm for low-level stuff talking... If he was truly interested, maybe he would already have started learning stuff in that direction?
Do you have experience in making that software-to-hardware (or low-level software) transition? Or, better yet, of taking a software only guy, and transitioning him to the low-level stuff?
Edit:
P.S. I'd love to hear from the responders what their own background is -- EE, CS, both?
At the end of the day, everything is an API.
Need to write code for an SPI peripheral inside a microcontroller? Well, get the datasheet or hardware manual, and look at the SPI peripheral. It's one, big, complex API.
The problem is that you have to understand the hardware and some basic EE fundamentals in order to comprehend what the API means. The datasheet isn't written by and for SW developers, it was written for hardware engineers, and maybe software engineers.
So it's all from the perspective of the hardware (face it - the microcontroller company is a hardware company filled with hardware/asic engineers).
Which means the transition is by no means simple and straightforward.
But it's not difficult - it's just a slightly different domain. If you can implement a study program, start off with Rabbit Semiconductor's kits. There's enough software there so a SW guy can really dig in with little effort, and the HW is easy to deal with because everything is wrapped in nice little libraries. When they want to do something complex they can dig into the direct hardware access and fiddle at the lower level, but at the same time they can do some pretty cool things such as build little webservers or pan/tilt network cameras. There are other companies with similar offerings, but Rabbit is really focused on making hardware easy for software engineers.
Alternately, get them into the Android platform. It looks like a unix system to them, until they want to do something interesting, and then they'll have the desire to attack that little issue and they'll learn about the hardware.
If you really want to jump in the deep end, go with an arduino kit - cheap, free compilers and libraries, pretty easy to start off with, but you have to hook wires up to do something interesting, which might be too big of a hurdle for a reluctant software engineer. But a little help and a few nudges in the right direction and they will be absolutely thrilled to have a little LED display that wibbles* like the nightrider lights...
-Adam
*Yes, that's a technical engineering term.
The best embedded programmers I've worked with are EE trained and learned SW on the job. The worst embedded developers are recent CS graduates who think SW is the only way to solve a problem. I like to think of embedded programming as the bottom of the SW pyramid. It's a stable abstraction layer/foundation that makes life easy for the app developers.
"Hard" is an extremely relative term. If you're used to thinking in the tight, sometimes convoluted way you need to for small embedded code (for example, you're a driver developer), then certainly it's not "hard".
Not to "bash" (no pun intended) shell scripters, but if you write perl and shell scripts all day, then it might very well be "hard".
Likewise if you're a UI guy for Windows. It's a different kind of thinking.
Why embedded development is "hard":
1) The context may switch to an interrupt between each machine instruction. Since high level language constructs may map to multiple assembly instructins, this might even be within a line of code, e.g. long var = 0xAAAA5555. If accessed in an interrupt service routine, in a 16 bit processore var might only be half set.
2) Visibility into the system is limited. You may not even have output to Hyperterm unless you write it yourself. Emulators don't always work that well or consistently (though they are way better than they used to be). You will have to know how to use oscilloscopes and logic analyzers.
3) Operations take time. For example, say your serial transmitter uses an interrupt to signal when it is time to send another byte. You could write 16 bytes to a transmit buffer, then clear interrupts and wonder why your message is never sent. Timing in general is a tricky part of embedded programming.
4) You are subject to subtle race conditions that occur only rarely and are very difficult to debug.
5) You have to read the manual. A lot. You can't make it work by fooling around. Sometimes 20 things have to be set up correctly to get what you are after.
6) The hardware doesn't always work or is easy to damage, and it takes a while to figure out that you broke it.
7) Software repairs in embedded systems are usually very expensive. You can't just update a web page. A recall can erase any profit you made on the device.
There are probably more but I've got this race condition to solve...
This is very subjective I guess, his reasons could be many. But if he's like me, I know where he's coming from. Let me explain.
In my career I've dedicated 6 years to the telecom industry, working a lot with embedding SDK middleware into low-end mobile phones etc.
Most embedded environments I've experienced are like harsh weather for a programmer, you constantly have to overcome limitations in resources etc. Some might find this a challenge and enjoy it for the challenge itself, some might feel close to "the real stuff" - the hardware, some might feel it limits their creativity.
I'm the kind who feels it limits my creativity.
I enjoy being back in Windows desktop environment and flap my wings with elaborate class designs, stretch my legs a few clockcycles extra, use unnecessary amounts of memory for diagnostics etc.
On certain embedded units in the past, I hardly had support for fseek() (an ANSI C standard file function). If lucky, a "watchdog" could give clues to where something crashed. Not to mention the pain of communicating with the user in single-threaded preemptive swamps.
Well, you know what I'm getting at. In my opinion it's not necessarily hard, but it's quite a leap, with potentially little reuse of your current experience.
Regards
Robert
There is a very real difference in mindset from user-level application development (ie, general purpose PC or Web applications) to hard deadline, real-time response application development (ie, the hardware/software interface).
Interrupts, instruction sets, context switching and hard resource constraints are relatively unknown to your average developer. I'm assuming here that your 'average developer' is not an Electrical/Electronic or other Engineer by training.
The transition for this developer you mention may be well outside his comfort zone. Some of us like stretching like that. Others of us may have decided the view isn't worth the climb.
Likewise, folks who've been in the hardware area (ie, Engineers) often have difficulty with the assumptions and language of software development.
These are gross generalities, of course, but hopefully give some insight.
He needs to be comfortable with the low-level stuff, but mostly for debugging and field issues. There is a serious learning curve depending on the architecture, but not impossible. On the other hand, the low-level code takes (in general) more time and debugging than higher-level code. So if you need to be going back to low-level all the time, then perhaps something isn't right in the design. Even for the embedded controls I've built, I spend the vast majority of time in high-level code. Although when you have issues, it is extremely advantageous to have a very good low-level knowledge.
I am an EE turned Software Engineer. I prefer programming low level. Most software developers classically trained that I know do not want to operate at this level they want apis to call. So for me it is a win win, I create the low level driver and api for them to use. There is a "new" degree, at least new since I went to college, called Computer Engineer. Hmm, it might be an electrical engineering degree not computer science, but it is a nice mix of software and digital hardware basics. The individuals that I have worked with from this field are much more comfortable with low level.
If the individual is not comfortable or willing then place them somewhere where they are comfortable. Let them do documentation or work on the user interface. If all of the work at the company requires low level work then this individual needs to do it or find another job. Dont sugar coat it.
I also think they will enjoy it once they get over the hump, the freedom you have at that level, not hindered by operating systems, etc. Recently I witnessed a few co-workers experience for the first time seeing their software run under simulation. Every net within the processor and other on chip peripherals. No you dont have a table on a gui (debugger) showing the current state of the memory, you have to look at the memory bus, look for the address you are interested in, look for a read or write signal and the data bus. I worry about the day that silicon arrives and they no longer have this level of visibility. Will be like an addict in detox.
Well, I cut my teeth on hardware when I started reading Popular Electronics at age 14 – this was BEFORE personal computers, in case you were wondering and if you weren’t well, you know anyway. lol
I’ve done the low level bit-bang stuff on the 8048/51 microprocessor, done PIC’s and some other single chip variations and of course Rabbit Semiconductor. (great if you're into C). That’s great (and fun) stuff; Yes, there is a different way of looking at things – not harder, but some of that information is a bit harder to come by as it isn’t as discussed as the software issues. (Of course, this depends on the circle of friends with which you associate, eh).
But, having said all of this, I want to remind you of a technology that started to bridge the gap for programmers into the world of hardware and has since become a very MAJOR player and that is the .NET micro framework. You can find information on this technology at the following;
http://msdn.microsoft.com/en-us/embedded/bb267253.aspx
It addresses some of the same issues that .NET web development addressed in that you can use some (quite a bit, actually) of your existing PC based knowledge in the new environments – Some caution, of course, as your target machine doesn’t have 4 GIG of RAM – it may only have 64K (or less)
Starting in version 2.5 of the .NET micro framework, you have access to networking and web services – way kewl, eh? It doesn’t stop there … Want to control the lights in your house? How about a temp recording station? All with the skills you already have. Well, mostly -- Check out the link.
The SDK plugs into your VisualStudio IDE. There are a number of “Development Kits” available for a very reasonable amount of cash – Now, what would normally take a big learning curve in components, building a circuit board and wiring up “stuff” can be done reasonably easy with a dev kit and some pretty simple code – Of course, you may need to do the occasional bit bang operation, but more and more sensor folks are providing .NET micro framework drivers – so, the hardware development may be closer than you think…
Hope it helps...
I like both. Embedded challenges me and really gets me going in a visceral way. Making something that affects the macro physical world is very satisfactory. But I've had to do a lot of catch up on the electrical/electronics end, since my bachelor's is in computer science. I've a pretty generalist background, where I studied ai, graphics, compilers, natural language, etc. Now I'm doing graduate work in embedded systems. The really tough part is adjusting to the lack of runtime facilities like an operating system.
Low-level embedded programming also tends to include low-level debugging. Which (in my experience) usually involves (at least) the use of an oscilloscope. Unless your colleague is going to be happy spending at least some of the time in physical contact with the hardware and thinking in terms of microseconds and volts, I'd be tempted to leave them be.
Agreed on the "hard" term is quite relative.
I would say different, as you would need to employ different development patterns that you won't use in other kind of environment.
The time constraint for instance could requires a learning curve.
However being curious, would be a quality for a developer, wouldn't be?
You are right in that anyone with enough knowledge not to feel completely lost in an area (over the hump?) will enjoy the challenges of learning something new.
I myself would feel quite nervous being moving to the level of instruction sets etc as there is a huge amount of background knowledge needed to feel comfortable in the environment.
It may make a difference if you are able to support the developer in learning how to do this. Having someone there you can ask and talk through issue with is a huge help in that sort of domain change.
It may be worth having the developer assigned to a smaller project with others as a first step and see how that goes. If he expresses enthusiasm to try another project, things should flow on from there.
I would say it is not any harder, it just requires a different knowledge set, different considerations.
I think that it depends on the way that they program in their chosen environment, and the type of embedded work that you're talking about.
Working on an embedded linux platform, say, is a far smaller jump than trying to write code on an 8 bit platform with no operating system at all.
If they are the type of person that has an understanding of what is going on underneath the api and environment that they are used to, then it won't be too much of a stretch to move into embedded development.
However, if their world view stops at the high level api that they've been using, and they have no concept of anything beneath that, they are going to have a really hard time.
As a (very) general statement if they are comfortable working on multithreaded applications they will probably be ok, as that shares some of the same issues of data volatility that you have when working on embedded projects.
With all of that said, I've seen more embedded programmers successfully working in PC development than I have the reverse. (of course I might not have seen a fair cross section)
"But when I talk to him about doing low-level programming, he simultaneously express interest and also doubt/uncertainty about joining the project." -- That means you let him try and you prepare to hire someone else in case he doesn't pass the learning curve.
i began as a SW engineer i'm now HW one !
the important is to understand how it works and to be motivated !