Building a Brain on a Silicon Chip
A chip developed by European scientists simulates the learning capabilities of the human brain.
A smart chip: Scientists in Europe are using conventional chip production techniques to create circuits that mimic the structure and function of the human brain. This early prototype has just 384 neurons and 100,000 synapses, but the latest version contains 200,000 neurons and 50 million synapses.
The FACETS Project
The goal of the FACETS (
Fast
Analog
Computing with
Emergent
Transient
States) project is to create a theoretical and experimental foundation for the realisation of novel computing paradigms which exploit the concepts experimentally observed in biological nervous systems.
Motivation
To understand the basic concepts behind these properties is essential for two reasons: The life-science point of view and the information-technology point of view.
- The first point of view has potential medical applications to cure brain and mind related diseases or even the longer-term goals to work towards neural prosthetic devices and artificial sensory organs.
- The second point of view could lead to new computing devices radically different from contemporary IT technology. Such devices could provide support for complex decision making processes like the one we are currently used to obtain only from human beings.
Modelling the Brain
In order to solve the model equations, all digital simulations rely on the repeated execution of simple operations on data stored in some kind of memory. This is fundamentally opposite to the realisation in the human nervous system, where 100 billions of neurons and about 1016 synapses operate in parallel in continuous time. There is an enormous gap between nature and simulation, which reaches a complexity in the order of 103 neurons in real-time with a simple integrate-and-fire model and conductance based synapses on the fastest available microprocessors.
Neural Hardware
[...] the only possibility to get a significant gain in simulation speed within the current decade is parallelization of dedicated analog circuits, which implement directly the processes in nerve cells. Dedicated hardware like analog ASICs can be optimized for parallelization. In FACETS' very large scale neural network systems, the cell based calculations will be done using analog models and the communication across medium or long distances using digital (spike-time) coding. With this approach, the final system size is only limited by the available resources and not by physical (signal degradation) or timing limitations.
New Neural Hardware from the FACETS Project
A new generation of neural network hardware is being developed by the
Fast Analog Computing with Emergent Transient States (FACETS) Project. The FACETS hardware will have 200,000 neurons with 50 million synapses built on a single silicon wafer. Current prototypes are running 100,000 times faster than their biological couterparts, allowing a full day of neural activity to be simulated in less than a second.
Physicist Karlheinz Meier, who coordinates the project, said,
We may then be able to make computing devices which are radically different and have amazing performance which, at some point, may approach the performance of the human brain – or even go beyond it!”
