CaDCR: An Efficient Cascaded Dynamic Collaborative Reasoning Framework for Intelligent Recognition Systems

To address the challenges of high computational cost and energy consumption posed by deep neural networks in embedded systems, this paper presents CaDCR, a lightweight dynamic collaborative reasoning framework. By integrating a feature discrepancy-guided skipping mechanism with a depth-sensitive early exit mechanism, the framework establishes hierarchical decision logic: dynamically selects execution paths of network blocks based on the complexity of input samples and enables early exit for simple samples through shallow confidence assessment, thereby forming an adaptive computational resource allocation strategy. CaDCR can both constantly suppress unnecessary computational cost for simple samples and satisfy hard resource constraints by forcibly terminating the inference process for all samples. Based on this framework, we design a cascaded inference system tailored for embedded system deployment to tackle practical deployment challenges. Experiments on the CIFAR-10/100, SpeechCommands datasets demonstrate that CaDCR maintains accuracy comparable to or higher than baseline models while significantly reducing computational cost by approximately 40–70% within a controllable accuracy loss margin. In deployment tests on the STM32 embedded platform, the framework’s performance matches theoretical expectations, further verifying its effectiveness in reducing energy consumption and accelerating inference speed.