Abstract: Matrix multipliers are computationally complex, and memory intensive algorithms used frequently in a variety of applications, such as deep-learning and scientific computations. Accelerating matrix multiplication involves an inter-play of algorithm-architecture co-design and context-specific design parameters. A performance optimizer intelligently arrives at the right combination of algorithm (203)-architecture specifications (201, 202) for the input design parameters that arrive during real-time for a target-specific design constraint. The run-time customization leads to optimal power-performance-area optimization.

Abstract: In one embodiment, one or more processor-readable media storing code representing instructions that when executed by one or more processors cause the one or more processors to receive a set of parameter values defining a boundary within a data space associated with a dataset. The dataset can represent signaling related to a test substance. A first metric can be defined based on a first portion of the dataset associated with a first region defined by the boundary. A second metric can be defined based on a second portion of the dataset associated with a second region defined by the boundary after the boundary is modified. The second region can be different than the first region.