Adaptive Streaming Perception using Deep Reinforcement Learning

Executing computer vision models on streaming visual data, or streaming perception is an emerging problem, with applications in self-driving, embodied agents, and augmented/virtual reality. The development of such systems is largely governed by the accuracy and latency of the processing pipeline. While past work has proposed numerous approximate execution frameworks, their decision functions solely focus on optimizing latency, accuracy, or energy, etc. This results in sub-optimum decisions, affecting the overall system performance. We argue that the streaming perception systems should holistically maximize the overall system performance (i.e., considering both accuracy and latency simultaneously). To this end, we describe a new approach based on deep reinforcement learning to learn these tradeoffs at runtime for streaming perception. This tradeoff optimization is formulated as a novel deep contextual bandit problem and we design a new reward function that holistically integrates latency and accuracy into a single metric. We show that our agent can learn a competitive policy across multiple decision dimensions, which outperforms state-of-the-art policies on public datasets.Comment: 19 pages, 17 figure

Survivable algorithms and redundancy management in NASA's distributed computing systems

The design of survivable algorithms requires a solid foundation for executing them. While hardware techniques for fault-tolerant computing are relatively well understood, fault-tolerant operating systems, as well as fault-tolerant applications (survivable algorithms), are, by contrast, little understood, and much more work in this field is required. We outline some of our work that contributes to the foundation of ultrareliable operating systems and fault-tolerant algorithm design. We introduce our consensus-based framework for fault-tolerant system design. This is followed by a description of a hierarchical partitioning method for efficient consensus. A scheduler for redundancy management is introduced, and application-specific fault tolerance is described. We give an overview of our hybrid algorithm technique, which is an alternative to the formal approach given

Experiments on Model-Based Software Energy Consumption Analysis Involving Sorting Algorithms

Although energy has become an important aspect in software development, little support exists for creating energy-efficient programs. One reason for that is the lack of abstractions and tools to enable the analysis of relevant properties involving energy consumption. This paper presents the results of some experiments involving the gathering, modelling, and analysis of energy-related information, in particular, the costs of executing certain parts of a software. We combine some existing free and open-source tools to carry out the experiments, extending one of them to handle energy information. Our experiments consider a comparison of energy consumption of Java implementations of the Bubble Sort, Insertion Sort and Selection Sort algorithms using different data structures. We show how to combine an energy measurement tool and a model analysis tool to carry such a comparison. Based on this support and on our experiments, we believe this is a first step to allow developers to start creating more energy-efficient software

Distributionally Robust Optimization

This chapter presents a class of distributionally robust optimization problems in which a decision-maker has to choose an action in an uncertain environment. The decision-maker has a continuous action space and aims to learn her optimal strategy. The true distribution of the uncertainty is unknown to the decision-maker. This chapter provides alternative ways to select a distribution based on empirical observations of the decision-maker. This leads to a distributionally robust optimization problem. Simple algorithms, whose dynamics are inspired from the gradient flows, are proposed to find local optima. The method is extended to a class of optimization problems with orthogonal constraints and coupled constraints over the simplex set and polytopes. The designed dynamics do not use the projection operator and are able to satisfy both upper- and lower-bound constraints. The convergence rate of the algorithm to generalized evolutionarily stable strategy is derived using a mean regret estimate. Illustrative examples are provided

Study of onboard expert systems to augment space shuttle and space station autonomy

The feasibility of onboard crew activity planning was examined. The use of expert systems technology to aid crewmembers in locating stowed equipment was also investigated. The crew activity planning problem, along with a summary of past and current research efforts, was discussed in detail. The requirements and specifications used to develop the crew activity planning system was also defined. The guidelines used to create, develop, and operate the MFIVE Crew Scheduler and Logistics Clerk were discussed. Also discussed is the mathematical algorithm, used by the MFIVE Scheduler, which was developed to aid in optimal crew activity planning

Efficiency metrics computing in combined sensor networks

This paper discusses the computer-aided design of combined networks for offices and building automation systems based on diverse wired and wireless standards. The design requirements for these networks are often contradictive and have to consider performance, energy and cost efficiency together. For usual office communication, quality of service is more important. In the wireless sensor networks, the energy efficiency is a critical requirement to ensure their long life, to reduce maintenance costs and to increase reliability. The network optimization problem has been solved under considering of overall-costs as objective and quality of service including throughput, delay, packet losses etc. with energy efficiency as required constraints. This can be achieved by a combination of different planning methods like placement of wired and wireless nodes, tracing of cabling systems, energy-efficient sensor management and event-based sampling. A successful application of these methods requires a combined harmonized design at different levels of the networks. This paper aims to demonstrate how these methods are realized in the network planning. These tools provide optimized wired and wireless topologies under considering of costs, distances, transmitted power, frequencies, propagation environments and obstacles given in computer-aided design compatible formats