The rising demand for lightweight, high-performance models on mobile and embedded platforms has accelerated the development of model compression techniques. Among these, combinational compression methods—which integrate multiple techniques such as pruning and quantization—offer complementary advantages over using individual methods alone. However, existing research typically focuses on specific combinations designed for a particular model architecture or task. These approaches often overlook the need for a general approach capable of identifying the optimal combination strategy, including the selection, sequence, and degree of applying compression methods. In this paper, we formalize the challenge of combining compression methods—specifically their selection, ordering, and compression degree—as a customized Markov Decision Process defined in a configurable compression space. To solve this, we introduce Compression Space Search (CSS), a practical RL-based framework for automatically and efficiently discovering optimal compression strategies. Experiments across CNN and transformer based vision models demonstrate that the proposed CSS achieves a 30 to 101 times reduction in bit operations while maintaining an accuracy drop of no more than 2%.

Over the past years, intelligent control tasks, such as deep neural networks (DNNs), have demonstrated significant potential in control systems. However, deploying intelligent control policies on heterogeneous computing platforms presents open challenges. These challenges extend beyond the apparent conflict between intensive computation and timing constraints and further encompass the interactions between task executions and complicated control performance. To address these challenges, this paper introduces RICH, a general and end-to-end approach to facilitate intelligent control tasks on heterogeneous computing architectures. RICH incorporates both offline Control-Oriented Computation and Resource Mapping (CCRM) and runtime Most Remaining Accelerator Segment Number First Scheduling (MRAF). Given the control tasks, the CCRM starts with balancing the computation workloads and processor resources with the goal of optimizing overall control performance. Subse-quently, the MRAF employs segment-level real-time scheduling to ensure the timely execution of tasks. Extensive experiments on the robotic arms applications (by hardware-in-the-loop simulator) demonstrate that the RICH can work as a general and end-to-end approach. These experiments reveal significant improvements in control performance, with enhancements of 50.7% observed for intelligent control applications deployed on heterogeneous computing platforms.

The rising popularity of AI applications is driving the adoption of heterogeneous computing architectures to handle complex computations. However, as these heterogeneous architectures grow more complex, optimizing the scheduling of multiple tasks and meeting strict timing constraints becomes significantly challenging. Current studies on real-time scheduling on heterogeneous processors lack agile and flexible simulation tools that can quickly adapt to varying system settings, leading to inefficiencies in system design. Additionally, the high costs associated with evaluating real-time performance in terms of human and facility efforts further complicate the development process. To address these challenges, this paper introduces a comprehensive hierarchical simulating approach and a corresponding simulator designed for flexible heterogeneous computing platforms. The simulator supports ideal or practical, off-the-shelf or customizable heterogeneous architectures, upon which the simulator can execute both parallel and dependent tasks. Utilizing this simulator, we present two case studies that were time-consuming previously but are now easily achieved by the proposed simulator. The first case study reveals the possibility of using policy-based reinforcement learning to explore novel scheduling strategies; the second explores the dominant processors within heterogeneous architectures, providing insights for optimizing the heterogeneous architecture design.

Despite the advancements in supporting artificial intelligence, accelerator-enabled computing architectures still struggle to meet strict timing constraints due to the complex interactions between CPU cores and accelerators. Although various scheduling and response-time analysis techniques have been developed, a significant gap remains between the conservative hard real-time schedulability (i.e., worst-case response times) and the average measured schedulability on real systems. This pessimism significantly limits the deployment of hard real-time tasks on accelerator-enabled computing platforms. To address this, we propose HARD, a real-time scheduling approach that integrates scheduling strategies, response time analysis, and practical scheduler designs for general accelerator-enabled computing platforms. Benefiting the subtask level segmented characteristics that are ignored by classic schedulers, the proposed HARD can significantly improve the theoretically guaranteed hard real-time schedulability. Extensive experiments on off-the-shelf Intel CPUs and NVIDIA GPUs show that HARD outperforms state-of-the-art scheduling and analysis approaches, delivering a 11.3% improvement in hard real-time schedulability and a remarkable 45.1 % reduction in pessimism.

In recent years, multiple data copies become popular in heterogeneous computing architectures. They enable parallel data transfer among diverse processing units. Tasks executed on such heterogeneous architectures often exhibit heightened re-source competitions and intricate task dependencies, posing challenges in meeting strict timing constraints. Due to the dominant roles of data copies in the heterogeneous architecture, effective scheduling and tight response time analysis could contribute to the timing performance of the entire heterogeneous computing system. In this work, we introduce MATCH, which offers realtime scheduling and end-to-end response time analysis for the multiple parallel data copies that are popular in mainstream heterogeneous architectures. We first identify the aggravated resource competition and task dependency from multiple data copies and comprehensive task execution patterns. Then, we provide a real-time scheduling strategy and cross-granularity schedulability analysis to deal with resource competition and task dependency. Extensive evaluation demonstrates that efficient scheduling and analysis on multiple parallel data copies can significantly improve the schedulability by 55.5%-144.4%. Additionally, experiments conducted on various scales of heterogeneous systems demonstrate that MATCH can significantly reduce pessimism in response time analysis by up to 22.8%-57.5%. Importantly, the proposed approach is compatible with existing scheduling approaches that do not consider multiple parallel data copies and are readily applied to off-the-shelf heterogeneous computing systems.

While accelerator-based heterogeneous architectures have gained traction in accelerating AI tasks, effectively managing them with stringent timing constraints remains a challenge. Although many scheduling and response time analysis approaches are proposed for multi-core or heterogeneous multi-core (i.e., big.LITTLE cores) processors, direct application of them to accelerator-based heterogeneous architectures with multiple CPUs and numerous processing units (PEs) often results in significant pessimism. This paper introduces real-time scheduling and comprehensive response time analysis from unit-level micro view to job-level macro view, for general accelerator-based heterogeneous architectures, greatly enhancing schedulability and utilization rates. We begin by establishing a general task execution pattern on heterogeneous architectures that integrates multiple CPU cores and various PEs. Subsequently, we present a real-time scheduling strategy and corresponding response time analysis based on this task execution pattern from micro to macro views. Through extensive experiments conducted on GEMM and AI workloads, our proposed scheduling and response time analysis significantly outperforms state-of-the-art scheduling algorithms, improving schedulability by 10.3% to 52.9%. Furthermore, experiments on NVIDIA GPU systems indicate a potential pessimism reduction of up to 30.7%. As we target general heterogeneous architectures, our approach can be readily applied to off-the-shelf accelerator-based heterogeneous computing systems, ensuring adherence to deadlines and enhancing schedulability.