In december 2008, a video post has been published on Abovetopsecret.com with the title “DARPA & IBM building a “global brain” “cognitive computer” for “monitoring people”. In this video, the leader of the IBM SyNAPSE project, Dharmendra Modha, talks about SyNAPSE.
SyNAPSE is not a project DARPA undertook lightly. Many attempts at large-scale neuromorphic engineering have been made in the past. None met their goals. As such, SyNAPSE owes its existence to a number of recent game-changing developments. From HP Labs, the discovery of the memristor was one such keystone innovation. It took Greg Snider’s 2007 work in Nanotechnology, however, to establish memristors as a viable platform for the implementation of self-organizing recurrent neural networks.
Dharmendra S Modha is the Principal Investigator in one of the three DARPA SyNAPSE grants, the one awarded to IBM. Modha is the Manager of the Cognitive Computing facility at IBM. Here is the full article from his blog.
Making Computers Based on the Human Brain
How the biology of gray matter is having an increasing influence on computer design
Dr. Snider and his colleagues at HP have built an integrated hybrid circuit with both transistors and memristors. Memristor crossbars are a very promising technology that can ultimately lead to building very dense hybrid chips, several times denser than synapses in the human cortex. Also, memristors have shown the potential to mimic the learning functions of synapses in neural networks. Memristors will the key technology that HP and its academic partner, Boston University, will leverage in the SyNAPSE grant.
On November 20, 2008, the NY Times has published a short article entitled “Hunting for a Brainy Computer”. Steve Lohr interviews the leader of the IBM team. IBM’s Blue Gene has been used to simulate large-scale neural models (see the Blue Brain Project, led by Henry Markram). However, it is easy to mix supercomputers, IBM, and SyNAPSE in a big pot, thinking that they are the same. In reality, the Blue Gene is the example of how not to simulate the brain. This machine, as large as a room, whose power consumption is the same as the sum of the brains of a small city, can barely simulate a cortical column. As this article does not stress much (unlike other cited in this blog), the hardware problem will be solved (hopefully) by nanotechnologies, in particular by porting to nano the immense number of synapses that will link the millions of neurons implemented in the chip. No comment on “Dorothy looking for the Wizard of Oz” and “Want a really intelligent digital assistant”… It is worth mentioning that even with a chip twice the density and half the power consumption that the one SyNAPSE seeks to have in seven years available TODAY in the hands of the best modelers in the world, it is hard to think that we have the necessary modeling skills to implement that is suggested below.
IEEE Spectrum online. Again, IBM appears all over the news. One of the main misconceptions of SyNAPSE is that, imagining of course the 3 companies involved in SyNAPSE succeed, the resulting chip will automatically result in better “MRAPs, UAVs, Mars Rovers”. This is of course not true. A very dense neural chip is 1/2 of the story. The ingredient that SyNAPSE needs to succeed is having meaningful neural models implemented on the chip. And this is where the other 1/2 of the competition will lie in the long (7 years) program.