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1. MaxTracker: Continuously Tracking the Maximum Computation Progress for Energy Harvesting ReRAM-based CNN Accelerators

   https://doi.org/10.1145/3477009  

   Qiu, Keni; Jao, Nicholas; Zhou, Kunyu; Liu, Yongpan; Sampson, Jack; Kandemir, Mahmut Taylan; Narayanan, Vijaykrishnan (October 2021, ACM Transactions on Embedded Computing Systems)

   null (Ed.)

   There is an ongoing trend to increasingly offload inference tasks, such as CNNs, to edge devices in many IoT scenarios. As energy harvesting is an attractive IoT power source, recent ReRAM-based CNN accelerators have been designed for operation on harvested energy. When addressing the instability problems of harvested energy, prior optimization techniques often assume that the load is fixed, overlooking the close interactions among input power, computational load, and circuit efficiency, or adapt the dynamic load to match the just-in-time incoming power under a simple harvesting architecture with no intermediate energy storage. Targeting a more efficient harvesting architecture equipped with both energy storage and energy delivery modules, this paper is the first effort to target whole system, end-to-end efficiency for an energy harvesting ReRAM-based accelerator. First, we model the relationships among ReRAM load power, DC-DC converter efficiency, and power failure overhead. Then, a maximum computation progress tracking scheme ( MaxTracker ) is proposed to achieve a joint optimization of the whole system by tuning the load power of the ReRAM-based accelerator. Specifically, MaxTracker accommodates both continuous and intermittent computing schemes and provides dynamic ReRAM load according to harvesting scenarios. We evaluate MaxTracker over four input power scenarios, and the experimental results show average speedups of 38.4%/40.3% (up to 51.3%/84.4%), over a full activation scheme (with energy storage) and order-of-magnitude speedups over the recently proposed (energy storage-less) ResiRCA technique. Furthermore, we also explore MaxTracker in combination with the Capybara reconfigurable capacitor approach to offer more flexible tuners and thus further boost the system performance. 

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2. When Green Computing Meets Performance and Resilience SLOs.

   https://doi.org/10.1109/DSN-S60304.2024  

   Qiu, H; Mao, W; Wang, C; Jha, S; Franke, H; Narayanaswami, C; Kalbarczyk, ZT; Basar, T; Iyer, R_K (January 2024, Institute of Electrical and Electronics Engineers)

   nd (Ed.)

   This paper addresses the urgent need to transition to global net-zero carbon emissions by 2050 while retaining the ability to meet joint performance and resilience objectives. The focus is on the computing infrastructures, such as hyperscale cloud datacenters, that consume significant power, thus producing increasing amounts of carbon emissions. Our goal is to (1) optimize the usage of green energy sources (e.g., solar energy), which is desirable but expensive and relatively unstable, and (2) continuously reduce the use of fossil fuels, which have a lower cost but a significant negative societal impact. Meanwhile, cloud datacenters strive to meet their customers’ requirements, e.g., service-level objectives (SLOs) in application latency or throughput, which are impacted by infrastructure resilience and availability. We propose a scalable formulation that combines sustainability, cloud resilience, and performance as a joint optimization problem with multiple interdependent objectives to address these issues holistically. Given the complexity and dynamicity of the problem, machine learning (ML) approaches, such as reinforcement learning, are essential for achieving continuous optimization. Our study highlights the challenges of green energy instability which necessitates innovative MLcentric solutions across heterogeneous infrastructures to manage the transition towards green computing. Underlying the MLcentric solutions must be methods to combine classic system resilience techniques with innovations in real-time ML resilience (not addressed heretofore). We believe that this approach will not only set a new direction in the resilient, SLO-driven adoption of green energy but also enable us to manage future sustainable systems in ways that were not possible before. 

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3. DTRL: Decision Tree-based Multi-Objective Reinforcement Learning for Runtime Task Scheduling in Domain-Specific System-on-Chips

   https://doi.org/10.1145/3609108  

   Basaklar, Toygun; Goksoy, A Alper; Krishnakumar, Anish; Gumussoy, Suat; Ogras, Umit Y (October 2023, ACM Transactions on Embedded Computing Systems)

   Domain-specific systems-on-chip (DSSoCs) combine general-purpose processors and specialized hardware accelerators to improve performance and energy efficiency for a specific domain. The optimal allocation of tasks to processing elements (PEs) with minimal runtime overheads is crucial to achieving this potential. However, this problem remains challenging as prior approaches suffer from non-optimal scheduling decisions or significant runtime overheads. Moreover, existing techniques focus on a single optimization objective, such as maximizing performance. This work proposes DTRL, a decision-tree-based multi-objective reinforcement learning technique for runtime task scheduling in DSSoCs. DTRL trains a single global differentiable decision tree (DDT) policy that covers the entire objective space quantified by a preference vector. Our extensive experimental evaluations using our novel reinforcement learning environment demonstrate that DTRL captures the trade-off between execution time and power consumption, thereby generating a Pareto set of solutions using a single policy. Furthermore, comparison with state-of-the-art heuristic–, optimization–, and machine learning-based schedulers shows that DTRL achieves up to 9× higher performance and up to 3.08× reduction in energy consumption. The trained DDT policy achieves 120 ns inference latency on Xilinx Zynq ZCU102 FPGA at 1.2 GHz, resulting in negligible runtime overheads. Evaluation on the same hardware shows that DTRL achieves up to 16% higher performance than a state-of-the-art heuristic scheduler. 

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4. Energy-Constrained Scheduling for Weakly Hard Real-Time Systems Using Standby-Sparing

   https://doi.org/10.1145/3631587  

   Niu, Linwei; Rawat, Danda B; Musselwhite, Jonathan; Gu, Zonghua; Deng, Qingxu (November 2023, ACM Transactions on Design Automation of Electronic Systems)

   For real-time embedded systems, QoS (Quality of Service), fault tolerance, and energy budget constraint are among the primary design concerns. In this research, we investigate the problem of energy constrained standby-sparing for both periodic and aperiodic tasks in a weakly hard real-time environment. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. For such kind of systems, we firstly propose several novel standby-sparing schemes for the periodic tasks which can ensure the system feasibility under tighter energy budget constraint than the traditional ones. Then based on them integrated approachs for both periodic and aperiodic tasks are proposed to minimize the aperiodic response time whilst achieving better energy and QoS performance under the given energy budget constraint. The evaluation results demonstrated that the proposed techniques significantly outperformed the existing state of the art approaches in terms of feasibility and system performance while ensuring QoS and fault tolerance under the given energy budget constraint. 

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5. Resilient execution of distributed X-ray image analysis workflows

   https://doi.org/10.3389/fhpcp.2025.1550855  

   Nguyen, Hai Duc; Bicer, Tekin; Nicolae, Bogdan; Kettimuthu, Rajkumar; Huerta, E A; Foster, Ian T (June 2025, Frontiers in High Performance Computing)

   Long-running scientific workflows, such as tomographic data analysis pipelines, are prone to a variety of failures, including hardware and network disruptions, as well as software errors. These failures can substantially degrade performance and increase turnaround times, particularly in large-scale, geographically distributed, and time-sensitive environments like synchrotron radiation facilities. In this work, we propose and evaluate resilience strategies aimed at mitigating the impact of failures in tomographic reconstruction workflows. Specifically, we introduce an asynchronous, non-blocking checkpointing mechanism and a dynamic load redistribution technique with lazy recovery, designed to enhance workflow reliability and minimize failure-induced overheads. These approaches facilitate progress preservation, balanced load distribution, and efficient recovery in error-prone environments. To evaluate their effectiveness, we implement a 3D tomographic reconstruction pipeline and deploy it across Argonne's leadership computing infrastructure and synchrotron facilities. Our results demonstrate that the proposed resilience techniques significantly reduce failure impact—by up to 500× —while maintaining negligible overhead (<3%). 

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