Top 10 blog posts for 2011

Merry Christmas! Buon Natale! Frohe Weihnachten! by Jakob Montrasio (cc) (from Flickr)
Merry Christmas! Buon Natale! Frohe Weihnachten! by Jakob Montrasio (cc) (from Flickr)

Happy Holidays.

I ranked my blog posts using a ratio of hits to post age and have identified with the top 10 most popular posts for 2011 (so far):

  1. Vsphere 5 storage enhancements – We discuss some of the more interesting storage oriented Vsphere 5 announcements that included a new DAS storage appliance, host based (software) replication service, storage DRS and other capabilities.
  2. Intel’s 320 SSD 8MB problem – We discuss a recent bug (since fixed) which left the Intel 320 SSD drive with only 8MB of storage, we presumed the bug was in the load leveling logic/block mapping logic of the drive controller.
  3. Analog neural simulation or digital neuromorphic computing vs AI – We talk about recent advances to providing both analog (MIT) and digital versions (IBM) of neural computation vs. the more traditional AI approaches to intelligent computing.
  4. Potential data loss using SSD RAID groups – We note the possibility for catastrophic data loss when using equally used SSDs in RAID groups.
  5. How has IBM researched changed – We examine some of the changes at IBM research that have occurred over the past 50 years or so which have led to much more productive research results.
  6. HDS buys BlueArc – We consider the implications of the recent acquisition of BlueArc storage systems by their major OEM partner, Hitachi Data Systems.
  7. OCZ’s latest Z-Drive R4 series PCIe SSD – Not sure why this got so much traffic but its OCZ’s latest PCIe SSD device with 500K IOPS performance.
  8. Will Hybrid drives conquer enterprise storage – We discuss the unlikely possibility that Hybrid drives (NAND/Flash cache and disk drive in the same device) will be used as backend storage for enterprise storage systems.
  9. SNIA CDMI plugfest for cloud storage and cloud data services – We were invited to sit in on a recent SNIA Cloud Data Management Initiative (CDMI) plugfest and talk to some of the participants about where CDMI is heading and what it means for cloud storage and data services.
  10. Is FC dead?! – What with the introduction of 40GbE FCoE just around the corner, 10GbE cards coming down in price and Brocade’s poor YoY quarterly storage revenue results, we discuss the potential implications on FC infrastructure and its future in the data center.


I would have to say #3, 5, and 9 were the most fun for me to do. Not sure why, but #10 probably generated the most twitter traffic. Why the others were so popular is hard for me to understand.


MIT builds analog synapse chip

2011 Wikimedia commons (400px-Synapse_Illustration_unlabeled.svg)
2011 Wikimedia commons (400px-Synapse_Illustration_unlabeled.svg)

Recently MIT announced a new brain chip, a breakthrough device that simulates a single brain synapse with an analog chip.

We have discussed before the digital nueromorphic chip activity going on (see my IBM introducing their SyNAPSE chip and Electro-human interface posts). However both those were digital, this new MIT chip is analog.  The chip uses ~400 transistors and was fabricated using VLSI processing.

But first please take our new poll:

Analog, whats that?

Given that the world has gone digital, analog devices may be foreign to most of us.  But analog dominated the way electronics worked for the first half of last century and were still pretty prominent during the last half.

Nowadays, such devices are used primarily in signal processing, and where streams of data are transformed from one mode to another (serial/deserializers).   An analog signal has a theoretically an infinite resolution (Wikipedia), which should make it closer to real life and may be why some stereophiles perfer records to CDs.

Neurons are analog devices

That being said, it’s a treat to see some new analog technology come out that’s better than digital implementations.  One would have to say that neural activity is by definition analog and as such, should make simulating brain activity much easier.

The advantage of analog can be seen in that the neural synapse is the connection between two neurons.  Information is transferred between the two neurons by the take up of Ions.  In the case of the MIT synapse chip, the same sort of process occurs but in this case information flows based on gradients of electronic potential.

In testament to the capabilities of the new synapse chip they were able to resolve a long standing debate in neuro-biology. The question was on how long term potentation (LTP) and long term depression (LTD) which enhances or depresses the information transfer across the synapse was accomplished in real neurons.  Previously, it had been postulated that LTP and LTD would depend on two different mechanisms in real cells. But there was one theory that said with a specific type of receptor, both LTP and LTD could be performed in a single way.

MIT researchers were able to configure their synapse-chip to mimic that new receptor and were able to show how LTP and LTD could work with this single receptor in the brain.

Onto the brain

Of course a single synapse is not much considering the brain has 100B neurons each with many 100’s if not 1000’s of synapses. But it’s a start.

Naturally, considering its built out of transistors using CMOS technology, it should follow Moore’s law and after 18 months or so we should have a chip with two synapses on it. Another 40 or so doublings more (~60 years from now in 2071), if Moore’s law holds, we can have a brain-chip with 100B neurons and 100T synapses on it.

Of course, this being a prototype, I suppose with today’s fabrication capable of  creating 40M transistors/chip, we may already be able to simulate 100K synapses and 100 neurons. Which means we should have a brain’s level of neurons and synapses in 30 doublings or ~2056.

Analog is better than biological

The other nice thing about analog logic and transistors, is that information processing in the brain-chip should be orders of magnitude faster than the brain’s biological processing.  Which is probably even more frightening.

The IBM SyNAPSE chip mentioned earlier was an all digital creation and had two chip cores, one provided “learning synapses” and the other “programmable synapses”.  This was probably an attempt to mimic neural processing in digital logic.

The analog brain-chip that MIT has invented, has no such distinction, supplying all synapse functionality in 400 transistors.   Nonetheless, any accurate simulation of neural processes can help us to understand how to mimic it better. The fact that we have an analog simulation neural processes should help us improve the digital simulation to more closely match the brain.


Not sure what we should call this chip, it’s certainly not neuromorphic, because it’s a real simulation of analog neural synapses not a digital approximation.  I would use synapse- chip but its already in use.  I kind of like the brain-chip but that may be stretching it a bit. Maybe the neuron-chip is best for now

Now that we know the date for the singularity, hopefully we can be ready to deal with whatever happens then.


IBM research introduces SyNAPSE chip

IBM with the help of a Columbia, Cornell, University of Wisconsin (Madison) and University of California creates the first generation of neuromorphic chips (press release and video) which mimics the human brain’s computational architecture implemented via silicon.  The chip is a result of Project SyNAPSE (standing for Systems of Neuromorphic Adaptive Plastic Scalable Electronics)

Hardware emulating wetware

Apparently the chip supports two cores one with 65K “learning” synapses and the other with ~256K “programmable” synapses.  Not really sure from reading the press release but it seems each core contains 256 neuronal computational elements.

Wikimedia commons (481px-Chemical_synapse_schema_cropped)
Wikimedia commons (481px-Chemical_synapse_schema_cropped)

In contrast, the human brains contains between 100M and 500M synapses (wikipedia) and has ~85 billion neurons (wikipedia). Typical human neurons have 1000s of synapses.

IBM’s goal is to have a trillion neuron processing engine with 100 trillion synapses occupy a 2-liter volume (about the size of the brain) and consuming less than one kilowat of power (about 500X the brains power consumption).

I want one.

IBM is calling such a system built out of neuromorphic chips a cognitive computing system.

What do with the system

The IBM research team has demonstrated some typical AI applications such as simple navigation, machine vision, pattern recognition, associative memory and classification applications with the chip.

Given my history with von Neuman computing it’s kind of hard for me to envision how synapses represent “programming” in the brain.  Nonetheless, wikipedia defines a synapse as a connection between any two nuerons which can take two forms electrical or chemical. A chemical synapse (wikipedia), can have different levels of strength, plasticity, and receptivity.  Sounds like this might be where the programmability lies.

Just what the “learning” synapses do, how they relate to the programmatical synapses and how they do it is another question entirely.

Stay tuned, a new, non-von Neuman computing architecture was born today.  Two questions to ponder

  1. I wonder if they will still call it artificial intelligence?
  2. Are we any closer to the Singularity now?