A Chip Control Method for Distributed Random Pulses in Resource-Constrained Networks

Abstract

This invention relates to the field of edge AI chip technology, specifically a chip control method for distributed random pulses in resource-constrained networks. It includes a heterogeneous neural computing core comprised of a random computation neural convolutional feature extractor and a micro binary calibration coprocessor configured within the edge AI chip. This invention uses a random pulse stream analyzer to monitor the statistical confidence level of the random pulse computation in real time. When the confidence level reaches a preset threshold, an early decision termination mechanism is triggered to prematurely stop the computation of the random computation neural convolutional feature extractor and transfer the computation result to the micro binary calibration coprocessor for decision calibration. This method effectively alleviates the inherent contradiction between the accuracy and latency of random computation, significantly improves the energy efficiency and response speed of the edge AI chip, and extends the battery life of resource-constrained network devices.

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