Have Skills, Will Travel Charles Armour

iPhone Ecosystem Presentation

I had the opportunity to give a presentation on the iPhone Ecosystem earlier this week for a local group called IT Gulf Coast, and wanted to share.  It’s fairly comprehensive from a high level, and is an excellent introduction to the different players and processes you might encounter while doing iPhone App development.  While most of the information is specific to native applications, I do touch on HTML 5.0 and its future in iPhone development.

Check out the slides here, and be sure to check out Wirehead Labs, Inc. too!  I’ll try to post up a video as soon as possible.

Technological Singularity – Today Only $500M!

This morning I read a great article from New Scientist on memristors and their biological equivalents, and boy did I get excited.  I’ve been following the convergence of biology and technology for quite some time as I’m interested in bringing about the singularity.  When the two fields converge enough, we’ll have the ability to fully simulate the biological computers that we’re so fond of (brains), and hence we’ll have a proper playing ground for true artificial intelligence.

Current leading research in this matter tops out at around 10 thousand neurons and 30 million synapses, which is a far cry from the 100 billion neurons and 150 trillion synapses in a human brain.  That computer is a 22.8 TFLOP machine, while the fastest supercomputer in the world currently is around 1100 TFLOPs.  That still only provides enough computing horsepower to handle simulation of 500 thousand neurons and 150 million synapses.  We’re a long, long way off.

Or are we?  Those systems are not designed from the ground up to simulate such things.  They use global memory and thread models, run in software rather than hardware, and are generally unsuited to the distributed computation that neuron simulation requires.  The physical capacity (scaling) isn’t too terrible either, as long as you’ve got large eyes.  Here are a few assumptions (based on this page) for calculations (please correct me if I’m off base here!):

• A neuron can be represented by 3 floats – 1 excitation threshold, 1 excitation state, and 1 cooling factor
• A synapse can be represented by 2 addresses and 2 floats – 1 source, 1 destination, 1 memory (per memristors), and 1 efficiency factor
• The human brain contains some 100 billion (1.0E11) neurons.
• The human brain contains some 150 trillion (1.5E14) synapses.

A float is either 32b or 64b, and an address in this context would most certainly be 64b.

With these numbers we can ascertain the memory requirements of a brain simulation system, and those would be:

• 100 billion neurons at 12 bytes each is 1.2E12 bytes, or 1.2 terabytes.
• 150 trillion synapses at 24 bytes each is 3.6E15 bytes, or 3.6 petabytes.

Let’s ignore the cost of processing and updating all this information for now, and see what it might cost to put together such a memory subsystem:

• Commodity pricing for DRAM is around $10/GB for the fast stuff (DDR3).
• You’d need around 4 million of these sticks (includes some wiggle room).  Can be had for about $40M.
• Commodity pricing for HDDs is around $0.10/GB for the big stuff, say 2TB drives.
• You’d need around 2000 of these HDDs (includes some wiggle room) for persistance.  Can be had for about $400K.

So we’re at a little over $40M just in memory.  Obviously you’d get better rates at these volumes, though!  Let’s double this price for backplanes and power supply, to a cool $80M in memory, power, cooling, and interconnects.  Remember that our interconnects are just passing synapse data over a 3D mesh like our brain, nothing toroidal or otherwise, and they benefit from extreme locality.

Now let’s investigate processing at 100Hz (suggested by many studies as the maximum neural firing rate) with the use of FPGA chips (so they can still be reprogrammed, but run in hardware):

• Xilinx Virtex-6 FPGAs run at (up to) 1.6Ghz and are suitable for stream processing on up to 8 DRAM modules at once.
• A 512Mbit memory module/chip with a 64 bit (or larger) interface could read and write its entire contents about 100 times a second.
• I don’t have pricing for the Virtex-6 chips, but let’s assume that with their 75% off volume pricing they’d cost around $100 each.
• You’d need about 4 million Virtex-6 chips to process all 4 petabytes of state 100 times per second.  Can be had for $400M (or just build a fab).

So there you have it, it’s possible to do brain simulation at the human brain scale for about $500 million dollars, $600 million if you decide you must have a synchronized clock system (which requires double the state memory for buffering.  If anyone has this kind of cash lying around, let me know and I’ll make it happen!*

If not, don’t worry!  I’m sure the government already has ten – these are a bargain compared to Stealth Bombers, and why build one when you can build ten at ten times the cost?

* I can’t be held responsible if a $500M machine just sits there and blinks a lot, I’m sure there are many secrets to the brain we haven’t discovered!

Cloud vs. Mesh (and Google Wave)

Lately I’ve been reading a lot of news about fairly high profile datacenters and cloud platforms suffering failures, even Google’s App Engine.  It made me wonder if it’s time to re-visit the idea of a mesh cloud, which for some reason seems to be stymied in “impossible” land.  To be precise, we need something to get App Engine (or Google Docs, etc) off their cloud and onto an unbreakable distributed system.

Just the term “mesh cloud” would probably make a venture capitalist drool with anticipation, but the basic technology required for such a system is just a distributed data store.  If you haven’t yet heard of Wuala, check out their site and service.  I think they’re on the forefront of a viable technology for a mesh cloud computing, and boy do I wish they’d hire me (yes, I applied)!  Given Wuala’s decentralized and distributed data store (with proper servers as well as a mesh backing your data), the issue becomes one of bandwidth.

Since bandwidth-constrained applications do poorly in meshes, my thoughts centered on collaboration and communication software like Google Wave rather than data-intense applications like video encoding or real time simulation.  It’s really unfortunate that Google Wave requires a centralized server for locking and updates, as that breaks the technology for distributed platforms that lack locking semantics.  Of course, that may not be the case as information about Wave is hard to come by (shame on Google)!

So the real holy grail would be a cloud-based wave-like application with free, perpetual persistence.  I’ve put some thought into such platforms and would really like to dig in, but to get started I’d need to implement the very basics for a secure platform – a trust hierarchy.  While I could roll my own PKI system, I’m looking for something more like OpenID with a PKI component.  Is there any such beast?  Should there be?

DD-WRT Wins

After receiving my new laptop, I was dismayed to find that it would not keep a connection to my wireless router for more than a few minutes at a time.  After confirming the issue was with the router (a Netgear WNR834B) by using a few other devices, I decided to upgrade its firmware.  Lo and behold, it’s at the latest version, which came out quite awhile ago (early 2008) .  Since re-flashing with the latest firmware didn’t help, I decided to take the plunge and Flash the router with DD-WRT.

The WNR834B is quite like a few Linksys models, but is hard to find info for.  After finding installation notes here, then finding the proper images here (v2.4sp1) or here (v2.4), the Tftpd32 software refused to upload the image.  It turns out that the Tftpd32 software would not work in Vista, but the other Windows software listed here (download) works great.  While there is a bit of cord jiggling and prayer involved, it was a painless and fairly simple process once I had the proper tools.

I now enjoy a faster, stabler wireless router that supports VPN functions, has nice data graphs and logging, and even allows me to SSH into it!