Abstract: Introduced here is an artificial intelligence system designed for machine learning. The system may be based on a neuromorphic computational model that learns spatial patterns in inputs using data structures called Sparse Distributed Representations (SDRs) to represent the inputs. Moreover, the system can generate signatures for these SDRs, and these signatures may be used to create definitions of classes or subclasses for classification purposes.

Abstract: Introduced here are integrated circuits (also referred to as “chips”) that can be implemented in a neural processing unit. At a high level, the goal of these chips is to provide higher performance for machine learning algorithms than conventional processing units would. To accomplish this, the neural processing unit can include multiple computing components, each of which is able to independently determine the overlap between encoded data provided as input and values stored in a memory.

Abstract: Integrated circuit components are described that are formed using selective epitaxy such that the integrated circuit components, such as transistors, are vertically oriented. These structures have regions that are doped in situ during selective epitaxial growth of the component body. These components are grown directly in electrical communication lines. Moreover, these components are adapted for use in memory devices and are believed to not require the use of shallow trench isolation.

Abstract: Integrated circuit components are described that are formed using selective epitaxy such that the integrated circuit components, such as transistors, are vertically oriented. These structures have regions that are doped in situ during selective epitaxial growth of the component body. These components are grown directly in electrical communication lines. Moreover, these components are adapted for use in memory devices and are believed to not require the use of shallow trench isolation.