Neuromorphic Architectures Beyond Moore's Law

Neuromorphic engineering seeks to emulate how the brain functions via computer chips and/or software in order to better carry out specific functions. The brain really is an amazing organ than can do certain tasks much better than any computer. A lot of the research being done in this field is building models that are inspired by how the brain functions. The military, for instance, is keen on developing this technology for better AI learning/recognition of reading material among numerous other potentially beneficial tasks.

Moore's law itself doesn't appear to be ending soon, at least when it comes to increasing the amount of transistors. However even with this boost in computer chip hardware, certain software measures are just not improving at the same rate. Neuromorphic engineering has the potential to exploit how the brain functions in order to increase the speed at which specific tasks are performed (such as visual recognition). Here's a short abstract describing the potential (PDF).

Within this period traditional scaling of transistors in CMOS technology, will have reached its physical limits. However, advances in nanoscale structures such as carbon nanotubes and semiconducting wires have the potential to add new functionality by augmenting traditional processing in nano-CMOS technologies. We will move slowly but steadily towards an era where breakthroughs in the field, will not be driven only by research aimed at exploiting and managing the exponential rate of digital transistor density in CMOS technology (Moore’s Law). Research and development in the field will strive towards benefits from methodologies that allow advances in the structural complexity of micro-systems.

From an architectural viewpoint, we take inspiration from Nature. Biological information processing systems employ dynamic matter and learning at all levels in an amazing network of complex structures of different scales, from the nano to the micro and macro. Both biological brains and sensory organs operate at performance levels set by fundamental physical limits, under severe constraints of size, weight and energy resources and they are indeed engineering marvels of heterogeneous integration and structural complexity across different physical scales,

Another abstract discuss the potential (PDF) of using memristors to model neurons.

Memristive nanodevices may fill the role of an electronic analog of biological synapses: they are essentially analog memories that can be switched between extreme states in 20 nanoseconds or less, yet maintain their state for years when power is removed. They can also be manufactured at biological scale densities (more than 1010 devices per cm2) and integrated with conventional CMOS.

A workshop just recently took place to discuss these novel computing advancements.