This invention discloses a parallel situational gravity calculation
chip and method based on in-memory computing, belonging to the field of
artificial intelligence chips and analog in-memory computing technology. The
chip includes: an event polarity input unit, which converts externally input event information loads (six-dimensional polarity vectors) into analog
voltage signals; a situational reference storage array, employing a
memristor cross-switch array, with each row storing a six-dimensional binary encoded vector of a reference situational type (such as the 64 hexagrams); a parallel gravity calculation unit, which performs large-scale parallel multiply-accumulate operations simultaneously in the analog domain, following the gravity calculation rule of "like lines cooperate, dissimilar lines repel," generating a multi-dimensional gravity intensity distribution; and a gravity readout and post-
processing unit, which converts the gravity distribution into situational awareness results and outputs them. This invention adopts an in-memory computing architecture, eliminating the "memory wall"
bottleneck of the traditional von Neumann architecture, achieving
nanosecond-level fully parallel gravity calculation and ultra-low
power consumption operation. It can be deployed as an independent situational awareness processor at sensor terminals, IoT terminals, and wearable devices.