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Autonomous Machines

We build systems for autonomous (computing) machines, which include but are not limited to drones, autonomous cars, and ground service robots.  Our group explores solutions across the system stack, spanning from applications down to the hardware running the code. More specifically, we look at how an autonomous sytem, such as a robot, interacts from the application down through the hardware stack with its environment (as shown below).

Autonomous Machine functioning diagram

We start by building end-to-end benchmarks, which are representative of real-world applications. We need benchmarks because real-world applications are usually too complicated for systematic and reproducible research, so we distill the applications into small and manageable benchmarks that capture the essence of the real-world application. These benchmarks become the starting ground for our system-level design. We examine these (micro) applications to determine their computational bottlenecks on existing systems and use that information to guide the design of future systems. We build simulators, analysis tools, and profilers, and use the insights we gather to guide the design of hardware and system software.

Select Publications

S. Krishnan, B. Boroujerdian, W. Fu, A. Faust, and V. J. Reddi, “Air Learning: An AI Research Platform for Algorithm-Hardware Benchmarking of Autonomous Aerial Robots,” Springer Machine Learning Journal, no. Special Issue on Reinforcement Learning for Real Life, Forthcoming. arXiv VersionAbstract
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We introduce Air Learning, an AI research platform for benchmarking algorithm-hardware performance and energy efficiency trade-offs. We focus in particular on deep reinforcement learning (RL) interactions in autonomous unmanned aerial vehicles (UAVs). Equipped with a random environment generator, AirLearning exposes a UAV to a diverse set of challenging scenarios. Users can specify a task, train different RL policies and evaluate their performance and energy efficiency on a variety of hardware platforms. To show how Air Learning can be used, we seed it with Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) to solve a point-to-point obstacle avoidance task in three different environments, generated using our configurable environment generator. We train the two algorithms using curriculum learning and non-curriculum-learning. Air Learning assesses the trained policies' performance, under a variety of quality-of-flight (QoF) metrics, such as the energy consumed, endurance and the average trajectory length, on resource-constrained embedded platforms like a Ras-Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to 79.43% longer trajectories in one of the environments. To understand the source of such differences, we use Air Learning to artificially degrade desktop performance to mimic what happens on a low-end embedded system. QoF metrics with hardware-in-the-loop characterize those differences and expose how the choice of onboard compute affects the aerial robot's performance. We also conduct reliability studies to demonstrate how Air Learning can help understand how sensor failures affect the learned policies. All put together, Air Learning enables a broad class of RL studies on UAVs. More information and code for Air Learning can be found here.
T. T. Nguyen and V. J. Reddi, “Deep Reinforcement Learning for Cyber Security,” ArXiv. 2019. Publisher's VersionAbstract
Paper
The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and large-scale. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This paper presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for cyber-physical systems, autonomous intrusion detection techniques, and multi-agent DRL-based game theory simulations for defense strategies against cyber attacks. Extensive discussions and future research directions on DRL-based cyber security are also given. We expect that this comprehensive review provides the foundations for and facilitates future studies on exploring the potential of emerging DRL to cope with increasingly complex cyber security problems.
B. Boroujerdian, H. Genc, S. Krishnan, W. Cui, A. Faust, and V. J. Reddi, “MAVBench: Micro Aerial Vehicle Benchmarking,” in Proceedings of the International Symposium on Microarchitecture (MICRO), 2018.Abstract
Paper

Unmanned Aerial Vehicles (UAVs) are getting closer to becoming ubiquitous in everyday life. Among them, Micro Aerial Vehicles (MAVs) have seen an outburst of attention recently, specifically in the area with a demand for autonomy. A key challenge standing in the way of making MAVs autonomous is that researchers lack the comprehensive understanding of how performance, power, and computational bottlenecks affect MAV applications. MAVs must operate under a stringent power budget, which severely limits their flight endurance time. As such, there is a need for new tools, benchmarks, and methodologies to foster the systematic development of autonomous MAVs. In this paper, we introduce the “MAVBench” framework which consists of a closed-loop simulator and an end-to-end application benchmark suite. A closed-loop simulation platform is needed to probe and understand the intra-system (application data flow) and inter-system (system and environment) interactions in MAV applications to pinpoint bottlenecks and identify opportunities for hardware and software co-design and optimization. In addition to the simulator, MAVBench provides a benchmark suite, the first of its kind, consisting of a variety of MAV applications designed to enable computer architects to perform characterization and develop future aerial computing systems. Using our open source, end-to-end experimental platform, we uncover a hidden, and thus far unexpected compute to total system energy relationship in MAVs. Furthermore, we explore the role of compute by presenting three case studies targeting performance, energy and reliability. These studies confirm that an efficient system design can improve MAV’s battery consumption by up to 1.8X.

Y. Zhu, et al., “Cognitive Computing Safety: The New Horizon for Reliability/The Design and Evolution of Deep Learning Workloads,” IEEE Micro, no. 1, pp. 15–21, 2017. Publisher's VersionAbstract
Paper

Recent advances in cognitive computing have brought widespread excitement for various machine learning–based intelligent services, ranging from autonomous vehicles to smart traffic-light systems. To push such cognitive services closer to reality, recent research has focused extensively on improving the performance, energy efficiency, privacy, and security of cognitive computing platforms.

Among all the issues, a rapidly rising and critical challenge to address is the practice of safe cognitive computing— that is, how to architect machine learning–based systems to be robust against uncertainty and failure to guarantee that they perform as intended without causing harmful behavior. Addressing the safety issue will involve close collaboration among different computing communities, and we believe computer architects must play a key role. In this position paper, we first discuss the meaning of safety and the severe implications of the safety issue in cognitive computing. We then provide a framework to reason about safety, and we outline several opportunities for the architecture community to help make cognitive computing safer.

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