This paper presents DCentNet, a novel decentralized multistage signal classification approach for biomedical data obtained from Internet of Things (IoT) wearable sensors, utilizing early exit point (EEP) to improve both energy e fficiency and processing speed. Traditionally, IoT sensor data is processed in a centralized manner on a single node, Cloud-native or Edge-native, which comes with several restrictions, such as significant energy consumption on the edge sensor and greater latency. To address these limitations, we propose DCentNet, a decentralized method based on Convolutional Neural Network (CNN) classifiers, where a single CNN model is partitioned into several sub-networks using one or more EEPs. Our method introduces encoder-decoder pairs at EEPs, which serve to compress large feature maps before transferring them to the next sub-network, drastically reducing wireless data transmission and power consumption. When the input can be confidently classified at an EEP, the processing can terminate early without traversing the entire network. To minimize sensor energy consumption and overall complexity, the initial sub-networks can be set up in the fog or on the edge. We also explore di fferent EEP locations and demonstrate that the choice of EEP can be altered to achieve a trade-o ff between performance and complexity by employing a genetic algorithm approach. DCentNet addresses the limitations of centralized processing in IoT wearable sensor data analysis, o ff ering improved e fficiency and performance. The experimental results of electrocardiogram (ECG) classification validate the success of our proposed method. With one EEP, the system saves 94.54% of wireless data transmission and a corresponding 21% decrease in complexity, while the classification accuracy and sensitivity remain almost una ffected and stay at their original levels. When employing two EEPs, the system demonstrates a sensitivity of 98.36% and an accuracy of 97.74%, concurrently leading to a 91.86% reduction in wireless data transmission and a reduction in complexity by 22%. DCentNet is implemented on an ARM Cortex-M4 based microcontroller unit (MCU).

This paper introduces TinySaver, an early-exit-like dynamic model compression approach which employs tiny models to substitute large models adaptively. Distinct from traditional compression techniques, dynamic methods like TinySaver can leverage the difficulty differences to allow certain inputs to complete their inference processes early, thereby conserving computational resources. Most existing early exit designs are implemented by attaching additional network branches to the model's backbone. Our study, however, reveals that completely independent tiny models can replace a substantial portion of the larger models' job with minimal impact on performance. Employing them as the first exit can remarkably enhance computational efficiency. By searching and employing the most appropriate tiny model as the computational saver for a given large model, the proposed approaches work as a novel and generic method to model compression. This finding will help the research community in exploring new compression methods to address the escalating computational demands posed by rapidly evolving AI models. Our evaluation of this approach in ImageNet-1k classification demonstrates its potential to reduce the number of compute operations by up to 90\%, with only negligible losses in performance, across various modern vision models.

Modern deep learning (DL) models necessitate the employment of scaling and compression techniques for effective deployment in resource-constrained environments. Most existing techniques, such as pruning and quantization are generally static. On the other hand, dynamic compression methods, such as early exits, reduce complexity by recognizing the difficulty of input samples and allocating computation as needed. Dynamic methods, despite their superior flexibility and potential for co-existing with static methods, pose significant challenges in terms of implementation due to any changes in dynamic parts will influence subsequent processes. Moreover, most current dynamic compression designs are monolithic and tightly integrated with base models, thereby complicating the adaptation to novel base models. This paper introduces DyCE, an dynamic configurable early-exit framework that decouples design considerations from each other and from the base model. Utilizing this framework, various types and positions of exits can be organized according to predefined configurations, which can be dynamically switched in real-time to accommodate evolving performance-complexity requirements. We also propose techniques for generating optimized configurations based on any desired trade-off between performance and computational complexity. This empowers future researchers to focus on the improvement of individual exits without latent compromise of overall system performance. The efficacy of this approach is demonstrated through image classification tasks with deep CNNs. DyCE significantly reduces the computational complexity by 23.5% of ResNet152 and 25.9% of ConvNextv2-tiny on ImageNet, with accuracy reductions of less than 0.5%. Furthermore, DyCE offers advantages over existing dynamic methods in terms of real-time configuration and fine-grained performance tuning.

In recent years, two competitive time series classification models, namely, ROCKET and MINIROCKET, have garnered considerable attention due to their low training cost and high accuracy. However, they require a large number of random 1-D convolutional kernels to comprehensively capture features, which is incompatible with resource-constrained devices. Despite the development of heuristic algorithms designed to recognize and prune redundant kernels, the inherent time-consuming nature of evolutionary algorithms hinders efficient evaluation. To effectively prune models, this paper removes redundant random kernels from a feature selection perspective by eliminating associating connections in the sequential classifier. Two innovative algorithms are proposed, where the first ADMM-based algorithm formulates the pruning challenge as a group elastic net classification problem, and the second core algorithm named P-ROCKET greatly accelerates the first one by bifurcating the problem into two sequential stages. Stage 1 of P-ROCKET introduces dynamically varying penalties to efficiently implement group-level regularization to delete redundant kernels, and Stage 2 employs element-level regularization on the remaining features to refit a linear classifier for better performance. Experimental results on diverse time series datasets show that P-ROCKET prunes up to 60% of kernels without a significant reduction in accuracy and performs 11 times faster than its counterparts. Our code is publicly available at https://github.com/ShaowuChen/P-ROCKET.