With the rapid increase in the research, development, and application of neural networks in the current era, there is a proportional increase in the energy needed to train and use models. Crucially, this is accompanied by the increase in carbon emissions into the environment. A sustainable and socially beneficial approach to reducing the carbon footprint and rising energy demands associated with the modern age of AI/deep learning is the adaptive and continuous reuse of models with regard to changes in the environment of model deployment or variations/changes in the input data. In this paper, we propose PreIndex, a predictive index to estimate the environmental and compute resources associated with model retraining to distributional shifts in data. PreIndex can be used to estimate environmental costs such as carbon emissions and energy usage when retraining from current data distribution to new data distribution. It also correlates with and can be used to estimate other resource indicators associated with deep learning, such as epochs, gradient norm, and magnitude of model parameter change. PreIndex requires only one forward pass of the data, following which it provides a single concise value to estimate resources associated with retraining to the new distribution shifted data. We show that PreIndex can be reliably used across various datasets, model architectures, different types, and intensities of distribution shifts. Thus, PreIndex enables users to make informed decisions for retraining to different distribution shifts and determine the most cost-effective and sustainable option, allowing for the reuse of a model with a much smaller footprint in the environment. The code for this work is available here: https://github.com/JEKimLab/AIES2024PreIndex

Timing predictability is a precondition for successful communication over a covert timing channel. Real-time systems are particularly vulnerable to timing channels because real-time applications can easily have temporal locality due to limited uncertainty in schedules. In this paper, we show that real-time applications can create hidden information flow even when the temporal isolation among the time partitions is strictly enforced. We then introduce an online algorithm that randomizes time-partition schedules to reduce the temporal locality, while guaranteeing the schedulability of, and thus the temporal isolation among, time partitions. We also present an analysis of the cost of the randomization on the responsiveness of real-time tasks. From an implementation on a Linux-based real-time operating system, we validate the analysis and evaluate the scheduling overhead as well as the impact on an experimental real-time system.

Modern generative techniques, deriving realistic data from incomplete or noisy inputs, require massive computation for rigorous results. These limitations hinder generative techniques from being incorporated in systems in resource-constrained environment, thus motivating methods that grant users control over the time-quality trade-offs for a reasonable “payoff” of execution cost. Hence, as a new paradigm for adaptively organizing and employing recurrent networks, we propose an architectural design for generative modeling achieving flexible quality. We boost the overall efficiency by introducing non-recurrent layers into stacked recurrent architectures. Accordingly, we design the architecture with no redundant recurrent cells so we avoid unnecessary overhead.

This paper presents a design framework for machine learning applications that operate in systems such as cyber-physical systems where time is a scarce resource. We manage the tradeoff between processing time and solution quality by performing as much preprocessing of data as time will allow. This approach leads us to a design framework in which there are two separate learning networks: one for preprocessing and one for the core application functionality. We show how these networks can be trained together and how they can operate in an anytime fashion to optimize performance.

Deeper neural networks, especially those with extremely large numbers of internal parameters, impose a heavy computational burden in obtaining sufficiently high-quality results. These burdens are impeding the application of machine learning and related techniques to time-critical computing systems. To address this challenge, we are proposing an architectural approach for neural networks that adaptively trades off computation time and solution quality to achieve high-quality solutions with timeliness. We propose a novel and general framework, AnytimeNet, that gradually inserts additional layers, so users can expect monotonically increasing quality of solutions as more computation time is expended. The framework allows users to select on the fly when to retrieve a result during runtime. Extensive evaluation results on classification tasks demonstrate that our proposed architecture provides adaptive control of classification solution quality according to the available computation time.

Learning techniques are advancing the utility and capability of modern embedded systems. However, the challenge of incorporating learning modules into embedded systems is that computing resources are scarce. For such a resource-constrained environment, we have developed a framework for learning abstract information early and learning more concretely as time allows. The intermediate results can be utilized to prepare for early decisions/actions as needed. To apply this framework to a classification task, the datasets are categorized in an abstraction hierarchy. Then the framework classifies intermediate labels from the most abstract level to the most concrete. Our proposed method outperforms the existing approaches and reference baselines in terms of accuracy. We show our framework with different architectures and on various benchmark datasets CIFAR-10, CIFAR-100, and GTSRB. We measure prediction times on GPUequipped embedded computing platforms as well.

This paper presents a new schedulability test for safety-critical software undergoing a transition from single-core to multicore systems - a challenge faced by multiple industries today. Our migration model consists of a schedulability test and execution model. Its properties enable us to obtain a utilization bound that places an allowable limit on total task execution times. Evaluation results demonstrate the advantages of our scheduling model over competing resource partitioning approaches, such as Periodic Server and TDMA.

The trend in the semiconductor industry toward multicore processors poses a significant challenge to many suppliers of safety-critical real-time embedded software. Having certified their systems for use on single-core processors, these companies may be forced to migrate their installed base of software onto multicore processors as single-core processors become harder to obtain. These companies naturally want to minimize the potentially high costs of recertifying their software for multicore processors. In support of this goal, we propose an approach to solving a fundamental problem in migrating legacy software applications to multicore systems, namely that of preventing conflicts among I/O transactions from applications residing on different cores. We formalize the problem as a partition scheduling problem that serializes I/O partitions. Although this problem is strongly NP-complete, we formulate it as a Constraint Programming (CP) problem. Since the CP approach scales poorly, we propose a heuristic algorithm that outperforms the CP approach in scalability.

Integrated Modular Avionics (IMA) architecture has been widely adopted by the avionics industry due to its strong temporal and spatial isolation capability for safety-critical real-time systems. The fundamental challenge to integrating an existing set of single-core IMA partitions into a multi-core system is to ensure that the isolation of the partitions will be maintained without incurring huge redevelopment and recertification costs. To address this challenge, we developed an optimized partition scheduling algorithm which considers exclusive regions to achieve the synchronization between partitions across cores. We show that the problem of finding the optimal partition schedule is NP-complete and present a Constraint Programming formulation. In addition, we relax this problem to find the minimum number of cores needed to schedule a given set of partitions and propose an approximation algorithm which is guaranteed to find a feasible schedule of partitions if there exists a feasible schedule of exclusive regions.

Hierarchical scheduling of periodic resources has been increasingly applied to a wide variety of real-time systems due to its ability to accommodate various applications on a single system through strong temporal isolation. This leads to the question of how one can optimize over the resource parameters while satisfying the timing requirements of real-time applications. A great deal of research has been devoted to deriving the analytic model for the bounds on the design parameter of a single resource as well as its optimization. The optimization for multiple periodic resources, however, requires a holistic approach due to the conflicting requirements of the limited computational capacity of a system among resources. Thus, this paper addresses a holistic optimization of multiple periodic resources with regard to minimum system utilization. We extend the existing analysis of a single resource in order for the variable interferences among resources to be captured in the resource bound, and then solve the problem with Geometric Programming (GP). The experimental results show that the proposed method can find a solution very close to the one optimized via an exhaustive search and that it can explore more solutions than a known heuristic method.

In hard real-time systems such as avionics, computer board level designs are typically customized to meet specific reliability and real time requirements. This paper focuses on computer-aided application-specific design of I/O architecture using PCI as an example. We have built a tool (ASIIST) that will enable engineers to explore design spaces at the I/O bus architecture level, performing analysis that incorporates bus protocols, to provide guarantees of real-time properties.

In complex hard real-time systems with tight constraints on system resources, small changes in one component of a system can cause a cascade of adverse effects on other parts of the system. We address the inherent complexity of making architectural decisions by raising the level of abstraction at which the analysis is performed. Our analysis approach gives the system architect a rigorous method for quickly determining which system architectures should be pursued, and it allows the architect to track and manage the cascading effects of subsystem/component changes in a comprehensive, quantitative manner. The end product is a virtual architecture analysis that systematically incorporates the inherent coupling among interacting system components that share limited system resources.