3results about How to "Reduce computing latency" patented technology

This invention discloses a method for parallelizing and pipelining a nested multiplier, relating to the field of intelligent warehousing technology. Based on the nested multiplication concept, the multiplication operation is decomposed into a formula to obtain the squaring operation of the high-bit part and the difference of squares plus shift operation of the low-bit part. Based on the characteristic of 2ab in the multiplication formula, the intermediate values ​​required for high-bit calculation are obtained by querying a preset low-bit square value table through the low-bit results. For batch data, multiple sets of data are processed simultaneously through a parallel architecture. The overall calculation process is divided into multiple pipeline stages, and continuous calculation is achieved through stage cascading. The pipeline output results of each parallel computing unit are collected, verified, and integrated to output a complete set of multiplication results. This invention achieves breakthroughs in hardware resources, computational latency, and batch throughput through the collaborative design of nested recursive simplification, pipeline reuse, and parallel architecture, providing an efficient solution for high real-time and high-parallelism digital computing scenarios.

This invention provides a method for accelerating the compression of intelligent models on edge terminals, relating to the field of edge intelligence. Its key feature is that it includes: acquiring real-time operating status data of the edge terminal and feature information of the current input data, wherein the real-time operating status data includes computing resource load, memory occupancy, battery level, and device temperature. The advantages of this invention are: by sensing the device status and input complexity in real time, and utilizing reinforcement learning to dynamically generate an optimal strategy combination including pruning, hybrid quantization, and early termination mechanisms, this method overcomes the limitations of traditional static compression, achieving a dynamic balance between computing power, accuracy, and energy consumption. It not only significantly reduces inference latency and memory usage but also adaptively adjusts based on battery level and temperature, effectively solving the problems of difficult deployment, high heat generation, and short battery life of large models on resource-constrained edge devices, and greatly improving the real-time performance and stability of intelligent applications.

The application is specifically an optimization method of an air unmanned aerial vehicle communication network based on AI, relates to the technical field of communication networks, and comprises intelligent sensing and data processing, an AI decision and optimization core, communication and task collaborative management, and resource and energy consumption optimization.In the application, a laser radar scans a canyon terrain in real time, a cutoff frequency is calculated in combination with a rectangular waveguide model, a transverse electric wave transmission mode is dynamically adjusted, terrain changes are predicted in advance and mode switching is triggered, a buffer interval and a rollback mechanism are matched, and signal interruption caused by a terrain-induced multipath trap can be avoided; when a canyon width suddenly changes, mode switching is started in advance, parameter updating is completed before the unmanned aerial vehicle reaches a critical area, the risk of communication interruption is reduced, the rollback mechanism can correct erroneous switching, and link continuity is ensured.