The vision for the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program is to develop electronic neuromorphic machine technology that scales to biological levels.
Current programmable machines are limited not only by their computational capacity, but also by an architecture requiring human-derived algorithms to describe and process information from their environment. In contrast, biological neural systems, such as a brain, autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations. Since real-world problems generally have many variables and nearly infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications. Useful and practical implementations, however, do not yet exist.
The vision for the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program is to develop electronic neuromorphic machine technology that scales to biological levels. SyNAPSE supports an unprecedented multidisciplinary approach coordinating aggressive technology development activities in the following areas: hardware, architecture, simulation, and environment.
The initial phase of SyNAPSE developed nanometer-scale electronic synaptic components capable of adapting connection strength between two neurons in a manner analogous to that seen in biological systems and simulated the utility of these synaptic components in core microcircuits that support the overall system architecture.
Continuing efforts will focus on hardware development through microcircuit development, fabrication process development, single chip system development, and multi-chip system development. In support of these hardware developments, SyNAPSE seeks to develop increasingly capable architecture and design tools, very large-scale computer simulations of the neuromorphic electronic systems to inform the designers and validate hardware prior to fabrication, and virtual environments for training and testing simulated and hardware neuromorphic systems.
Dr. Gill A. Prattgill.email@example.com