Yes yes yes ... looks great :) ...

The 64 cores version is still out of reach though :(

Maybe, but are they also accesible for the programmer? I was greatly disappointed when I learnt that the praised and "powerful" GPU of the Raspberry Pi is locked down by NDAs and NOT available for the programmer for OpenCL or GPGPU. I think the same is true for the PowerVR.

I have looked around quite a while and have not found a readily available board with a GPU that could be programmed (OpenCL) and is powerful enough for real-time image/vision processing. Not sure about the Tegra 4 .... ?

I really like the parallella project. Due to its low power consumption (2 watts for the 64-core version), it is the only option to bring significant processing power to mobile devices (e.g. mobile robots/quadrocopter/drones) and would be ideally suited to implement machine vision and neural network/machine learning algorithms for those mobile devices.

That said, their kickstarter initiative has some serious flaws:

1. They are only offering the 16-core version for a goal of $750k. The much more interesting 64-core version is available only if a whopping $3m goal is met. Way out of reach for such a specialized interest project. And everyone who reads information about the parallella reads about the "sexy" 64-core version everywhere but can only fund the "just nice" 16-core version. From the comments it is clear: everyone wants the 64-core version.
2. There is only one interesting pledge: $99 for the 16-core version. No addons. No extras etc.
3. The information from adapteva is lacking. Only today they made the documentation available. But still there are no demos and dozens of questions in the comments which are unaswered.

Compare this to a greatly successful campaign like for example the Digispark (a low cost "mini-arduino"): a lower easily reachable goal, lots and lots of extras and addons developed together and in response to the backers and a constant information and communication with the backers. I wanted to spend $20 on this project but finally spent $70 because of all the addons and how responsive the team was to the backers. Digispark achieved more than 6000% of its initial goal!

That said, what would I suggest for the Parallella kickstarter:

1. Go for the 64-core version. Bring the goal from $3m down to say $1.5m by dropping the 16-core version (should save almos $1m) and some bank loan (if you can present >1000 backers who pay >$1.5m that should be no problem.
2. Offer more than just a 64-core parallella for $199. Offer special version for a higher price. Offer a dual-64-core version (with two epiphanies on it). Offer a "compute cluster": a little laser cut box with a network, a power supply and slots for up to 8 parallellas. Offer those cluster equipped with 1-8 parallellas. Offer a "machine vision" parallella with a camera sensor attached to it .. and so on ....
3. Be more open and communicating with the community. Answer all questions in the comments. Put up some polls what backers want. Provide demos/tutorials etc.

Please don't take this personally. But i would really like to see this project succeed. .... and I want machine vision and a neural network brain for my quadrocopter (yep, world domination ... that's the plan!) ;)

It always comes down to the application: the i7 and i3 you mention consume how many watts? ... Impossible if the "real-time image processing" mentioned above should be done on a mobile device (mobile robot/drone). The 64-core epiphany only consumes 2 watts (in words: TWO!)!

Yes, that's true. But unfortunately i cannot plug your Radeon or GTX into my mobile robot or quadrocopter in order to give them machine vision or neural networks/machine learning "brains" (at least not with some serious improvements in battery technology!).

So, what are the alternatives to bring the current vision algorithms to mobile devices/robots? The Parallella is the only option I am aware of.

For these types of mobile applications, you should rather compare the Parallella with Raspberry Pi or Arduino. And guess who wins this performance comparison! ;)

100nm process? ... Well, if you had read the information provided you would know that the 16-core version from the kickstarter is done in a 65nm process and the 64-core version is done in the 28nm process in cooperation with Globalfoundries.

And for the GPUs: yes, i know that a modern GPU (or even a core i7) is more powerful. But, I unfortunately I cannot plug a modern GPU into my mobile robot/drone/quadrocopter in order to do things like real-time vision processing/neural networks/machine learning/AI. The epiphany consumes something between 2-5 Watts (in words: TWO watts for 64-cores). I am currently not aware of anything coming close to the performance of the parallella for the mobile vision processing applications mentioned above.

PS: I know that the raspberry pi has quite a powerful GPU. But its GPU is locked down by NDAs and NOT accessible for OpenCL oder GPGPU.