SyNAPSE Chip: “Someday, you’ll work for ME!”

SyNAPSE Project Chip

SyNAPSE Project AI Neuromorphic Chip

IBM’s Aug 18th Press Release announced another significant milestone for the DARPA SyNAPSE project, the world’s best funded and arguably the “most likely to succeed” approach to creating a general artificial intelligence.

The release notes that the new chips represent a departure from traditional models of computing:

…. cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.

To do this, IBM is combining principles from nanoscience, neuroscience and supercomputing as part of a multi-year cognitive computing initiative. The company and its university collaborators also announced they have been awarded approximately $21 million in new funding from the Defense Advanced Research Projects Agency (DARPA) for Phase 2 of the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project.

As we’ve noted here many times, another remarkable project is the Blue Brain Project in Europe spearheaded by Dr. Henry Markram.     That team has joined with many others and is in the process of applying to the European Union for substantial funding – perhaps as much as 1.6 billion dollars.    Although Blue Brain tends to shy away from stating that their objective is a general artificial intelligence,  I would argue that they should have that goal and also that they are much more likely to be funded by stating that goal in no uncertain terms.

Unfortunately there remain many both in and outside of technology circles who believe the search for a general artificial intelligence is either dangerous or a waste of time and money.   Both these scenarios are possible but unlikely.   Sure, intelligence can be dangerous but given human history compared to technology history it seems odd to argue that we are more likely to create a Frankenstein than a helpful machine process.    Computers don’t kill people, people kill people.

In terms of a waste of time and money, clearly we humans have overrated our intelligence for some time – probably since the beginning of self-awareness.   There are few rational reasons to reject the idea that we cannot duplicate processes that are similar to our own thinking in a machine.   The advantages of machine based intelligence are likely to be  substantial – probably on the order of a new human age with vastly improved resource efficiency, poverty reduction, and more.  Thus the costs – currently measured in the low tens of millions – pale in comparison to almost all other government projects – many with massively dubious and negative ROIs.

SyNAPSE Update from Dr. Dharmendra Modha’s Team

Dr. Dharmendra Modha and his SyNAPSE gang recently published an excellent paper about “Cognitive Computing” that updates what appears to be excellent progress in the effort to create a general artificial intelligence:

One of the paper’s most notable items asserts that within a decade the project expects to have the computational scale needed for human level modelling, though it also notes that this is not the same as creating a model of the human brain – this may require computational structures yet to be invented.    However on balance it would seem the SyNAPSE project continues to build on their core assumptions, taking us ever closer to the holy grail of technology – a general artificial intelligence.

More at Dr. Modha’s blog , where we learn more about the new approaches the SyNAPSE team at IBM will take in an effort to achieve human quality cognition in a machine:

18 Aug 2011: Today, IBM (NYSE: IBM) researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition. The technology could yield many orders of magnitude less power consumption and space than used in today’s computers.

In a sharp departure from traditional concepts in designing and building computers, IBM’s first neurosynaptic computing chips recreate the phenomena between spiking neurons and synapses in biological systems, such as the brain, through advanced algorithms and silicon circuitry. Its first two prototype chips have already been fabricated and are currently undergoing testing.

Called cognitive computers, systems built with these chips won’t be programmed the same way traditional computers are today. Rather, cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.

IBM’s Artificial Intelligence – is the cat brain out of the bag or not?

We’ve profiled two of the world’s most promising AI efforts here at Technology Report.     Blue Brain in Switzerland and DARPA SyNAPSE here in the USA, a newer project that appears to be getting better funding thanks to backing from the US Defense Department.   Both of these projects rely on IBM supercomputers for their simulations of neurons and their interactions, and both are optimistic about the potential to develop thinking machines within the next decade.

The project leader of Blue Brain, Dr. Henry Markram, has been very vocal and very critical of claims by  the IBM team leader Dr. Dharmendra S. Modha.    Markram’s concerns are expressed here in his Technology Report guest post about the IBM project claims.

We asked Dr. Modha for a response but didn’t hear back, so I’d like to refer folks to the Modha blog here, especially to the post called ‘The Cat is Out of the bag and BlueMatter”, which details progress in the SyNAPSE project and explains the claims made that they are simulating brain activity that is roughly equivalent to that we’d see from a cat.  Here’s an excerpt from that post:

Towards this end, we are announcing two major milestones.

First, using Dawn Blue Gene / P supercomputer at Lawrence Livermore National Lab with 147,456 processors and 144 TB of main memory, we achieved a simulation with 1 billion spiking neurons and 10 trillion individual learning synapses. This is equivalent to 1,000 cognitive computing chips each with 1 million neurons and 10 billion synapses, and exceeds the scale of cat cerebral cortex. The simulation ran 100 to 1,000 times slower than real-time.

Second, we have developed a new algorithm, BlueMatter, that exploits the Blue Gene supercomputing architecture to noninvasively measure and map the connections between all cortical and sub-cortical locations within the human brain using magnetic resonance diffusion weighted imaging. Mapping the wiring diagram of the brain is crucial to untangling its vast communication network and understanding how it represents and processes information.

Finally, here is an excellent presentation by Dr. Modha that outlines in simple terms what they are trying to do with DARPA SyNAPSE, which is build a human scale brain by 2018.

US Military killed the Biologically-Inspired Cognitive Architectures (BICA) Project without explanation

It is somewhat tempting to think like a conspiracy theory buff and suggest that the Biologically-Inspired Cognitive Architectures project “BICA” – a major effort to create artificial intelligence – has succeeded and gone off the record rather than been cancelled by the US Government.

However the idea that BICA has simply been “cancelled” in favor of newer approaches seems far more likely, especially given the new focus of the DARPA SyNAPSE project we’ve discussed here at Technology Report several times before.    It appears that the more general and decentralized approach of BICA has been replaced with a more collaborative and engineered approach taken in the DARPA SyNAPSE project.

The Defense Advanced Research Projects Agency (DARPA) is one of the world’s best funded advanced technology research groups. DARPA’s most impressive accomplishment to date has been to fund the prizes that have inspired several university groups to create fully autonomous vehicles that can navigate both city traffic and complex off the road tracks without any human controls.

BICA Project:


DARPA SyNAPSE Project Summary

Today we have a guest post with permission from Max over at the “Neurdons” blog, written by a group working on the DARPA SyNAPSE project we have discussed here before.   SyNAPSE seeks to create a fully functional artificial intelligence.

This piece was written by Ben Chandler, an AI researcher with the SyNAPSE project:


First the facts: SyNAPSE is a project supported by the Defense Advanced Research Projects Agency (DARPA). DARPA has awarded funds to three prime contractors: HPHRL and IBM. The Department of Cognitive and Neural Systems at Boston University, from which the Neurdons hail, is a subcontractor to both HP and HRL. The project launched in early 2009 and will wrap up in 2016 or when the prime contractors stop making significant progress, whichever comes first. ‘SyNAPSE’ is a backronym and stands for Systems of Neuromorphic Adaptive Plastic Scalable Electronics. The stated purpose is to “investigate innovative approaches that enable revolutionary advances in neuromorphic electronic devices that are scalable to biological levels.”

SyNAPSE is a complex, multi-faceted project, but traces its roots to two fundamental problems. First, traditional algorithms perform poorly in the complex, real-world environments that biological agents thrive. Biological computation, in contrast, is highly distributed and deeply data-intensive.  Second, traditional microprocessors are extremely inefficient at executing highly distributed, data-intensive algorithms. SyNAPSE seeks both to advance the state-of-the-art in biological algorithms and to develop a new generation of nanotechnology necessary for the efficient implementation of those algorithms.

Looking at biological algorithms as a field, very little in the way of consensus has emerged. Practitioners still disagree on many fundamental aspects. At least one relevant fact is clear, however. Biology makes no distinction between memory and computation. Virtually every synapse of every neuron simultaneously stores information and uses this information to compute. Standard computers, in contrast, separate memory and processing into two nice, neat boxes. Biological computation assumes these boxes are the same thing. Understanding why this assumption is such a problem requires stepping back to the core design principles of digital computers.

The vast majority of current-generation computing devices are based on the Von Neumann architecture. This core architecture is wonderfully generic and multi-purpose, attributes which enabled the information age. Von Neumann architecture comes with a deep, fundamental limit, however. A Von Neumann processor can execute an arbitrary sequence of instructions on arbitrary data, enabling reprogrammability, but the instructions and data must flow over a limited capacity bus connecting the processor and main memory. Thus, the processor cannot execute a program faster than it can fetch instructions and data from memory. This limit is know as the “Von Neumann bottleneck.”

In the last thirty years, the semiconductor industry has been very successful at avoiding this bottleneck by exponentially increasing clock speed and transistor density, as well as by adding clever features like cache memorybranch predictionout-of-order execution and multi-core architecture. The exponential increase in clock speed allowed chips to grow exponentially faster without addressing the Von Neumann bottleneck at all. From the user perspective, it doesn’t matter if data is flowing over a limited-capacity bus if that bus is ten times faster than that in a machine two years old. As anyone who has purchased a computer in the last few years can attest, though, this exponential growth has already stopped. Beyond a clock speed of a few gigahertz, processors dissipate too much power to use economically.

Cache memory, branch prediction and out-of-order execution more directly mitigate the Von Neumann bottleneck by  holding frequently-accessed or soon-to-be-needed data and instructions as close to the processor as possible. The exponential growth in transistor density (colloquially known as Moore’s Law) allowed processor designers to convert extra transistors directly into better performance by building bigger caches and more intelligent branch predictors or re-ordering engines. A look at the processor die for the Core i7 or the block diagram of the Nehalem microarchitecture on which Core i7 is based reveal the extent to which this is done in modern processors.

Multi-core and massively multi-core architectures are harder to place, but still fit within the same general theme. Extra transistors are traded for higher performance. Rather than relying on automatic mechanisms alone, though, multi-core chips give programmers much more direct control of the hardware. This works beautifully for many classes of algorithms, but not all, and certainly not for data-intensive bus-limited ones.

Unfortunately, the exponential transistor density growth curve cannot continue forever without hitting basic physical limits. At this point, Von Neumann processors will cease to grow appreciably faster and users won’t need to keep upgrading their computers every couple years to stave off obsolence. Semiconductor giants will be left with only two basic options: find new high-growth markets or build new technology.  If they fail at both of these, the semiconductor industry will cease to exist in its present, rapidly-evolving form and migrate towards commoditization. Incidentally, the American economy tends to excel at innovation-heavy industries and lag other nations in commodity industries. A new generation of microprocessor technology means preserving American leadership of a major industry. Enter DARPA and SyNAPSE.

Given the history and socioeconomics, the “Background and Description” section from the SyNAPSE Broad Agency Announcement is much easier to unpack:

Over six decades, modern electronics has evolved through a series of major developments (e.g., transistors, integrated circuits, memories, microprocessors) leading to the programmable electronic machines that are ubiquitous today. Owing both to limitations in hardware and architecture, these machines are of limited utility in complex, real-world environments, which demand an intelligence that has not yet been captured in an algorithmic-computational paradigm. As compared to biological systems for example, today’s programmable machines are less efficient by a factor of one million to one billion in complex, real-world environments. The SyNAPSE program seeks to break the programmable machine paradigm and define a new path forward for creating useful, intelligent machines.

The vision for the anticipated DARPA SyNAPSE program is the enabling of electronic neuromorphic machine technology that is scalable to biological levels. Programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment. In contrast, biological neural systems (e.g., brains) autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations. Since real world systems are always many body problems with infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications—but useful and practical implementations do not yet exist.

SyNAPSE seeks not just to build brain-like chips, but to define a fundamentally distinct form of computational device. These new devices will excel at the kinds of distributed, data-intensive algorithms that complex, real-world environment require. Precisely the kinds of algorithms that suffer immensely at the hands of the Von Neumann bottleneck.

SyNapse and Blue Brain Projects Update

As noted before I think the two most promising “Artificial Intelligence” projects are Blue Brain and DARPA SyNAPSE and I’m happy to see in this Boston blog “Neurdon” by some of the SyNAPSE project folks a few of the DARPA bucks going to elaborate on some of the technical goals of the SyNAPSE project:

SyNAPSE seeks not just to build brain-like chips, but to define a fundamentally distinct form of computational device. These new devices will excel at the kinds of distributed, data-intensive algorithms that complex, real-world environment require…

It’s very exciting stuff this “build a brain” competition.  Although I think the theoretical approach taken by Blue Brain is more consistent with what little we know about how brains work, I’d guess SyNAPSE’s access to DARPA funding will give it the long term edge in terms of delivering a functional thinking machine in the 15-20 year time frame most artificial intelligence researches believe we’ll need for that ambitious goal.

My optimism is greater than many because I think humans have rather dramatically exaggerated the complexity of their own feeble mental abilities by a quite a … bit, and I’d continue to argue that consciousness is much more a function of quantity than quality.

Another promising development in the artificial brain area is in Spain where  Blue Brain project partner universities are working on the project:  Cajal Blue Brain