Abstract: A Self-Explaining Decision Architecture (SEDA) for machine learning-based decision-making systems capable of generating intuitive explanations for its decisions in real time. SEDA makes use of a feature extraction subsystem and a sequence interpretation subsystem to identify patterns in data followed by a decision generation subsystem that determines appropriate actions based on those patterns. Internal state information from each of these subsystems is used to generate explanations of the system's decisions. Using this information to create explanations provides insight as to the data elements the system focused on when making decisions as well as the reasoning that was used. In at least one embodiment the system uses deep learning components including a combined convolutional neural network and long short-term memory network with attention mechanisms.

Abstract: A reconfigurable processor including a plurality of reconfigurable execution units, a memory, an instruction queue, a configuration selection unit, and a configuration loader. The memory stores a plurality of steering vector processing hardware configurations for configuring the reconfigurable execution units. The instruction queue stores a plurality of instructions to be executed by at least one of the reconfigurable execution units. The configuration selection unit analyzes the instructions stored in the instruction queue and chooses one of the steering vector processing hardware configurations. The configuration loader determines whether one of the reconfigurable slots is available and reconfigures at least one of the reconfigurable slots with at least a part of the chosen steering vector processing hardware configuration responsive to at least one of the reconfigurable slots being available.