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1. VPP: The Vulnerability-Proportional Protection Paradigm Towards Reliable Autonomous Machines

   Zishen, Wan; Yiming, Gan; Bo, Yu3; Arijit, Raychowdhury; Yuhao, Zhu (June 2023, workshop)

   The next ubiquitous computing platform, after personal computers and smartphones, is likely one of the autonomous natures, such as drones, robots, and self-driving cars, which have moved from mere lab concepts to permeating almost every aspect of our soci- ety [16, 20, 25]. Behind the proliferation of autonomous machines is the critical need to ensure reliability [7, 22–24]. Almost every vendor, be it in the software, hardware, or systems segment, has to conform to functional safety standards when shipping products for automotives. Today’s resiliency solutions to autonomous machines, however, all make fundamental trade-offs between resiliency and cost, which manifests as high overhead in performance, energy, and chip area. For instance, hardware modular redundancy provides high safety but more than doubles the area and energy cost [1]. The reason is that today’s solutions are of the “one-size-fits-all” nature: they use the same protection scheme throughout the entire computing stack of autonomous machines. As a result, they have to accommodate the least robust component, leading to a high protection overhead. The insight of this paper is that for a resiliency solution to pro- vide high protection coverage while introducing little cost, we must exploit the inherent robustness variations in the domain-specific autonomous machine computing. In particular, we show that the different autonomous machine kernels differ significantly in their inherent robustness and performance. Building on top of that, we propose a Vulnerable-Proportional Protection (VPP) design paradigm, in which the protection budget, be it spatially (e.g., modular re- dundancy) or temporally (e.g., re-execution), should be inversely proportional to the inherent robustness of a task in the autonomous machine system. In stark contrast to the existing “one-size-fits-all” strategy, VPP wisely allocates the protection budget, thus achieving the same protection coverage with little overhead, which provides a blueprint design paradigm towards reliable autonomous machines 

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2. Key issues review: useful autonomous quantum machines

   https://doi.org/10.1088/1361-6633/ad8803  

   Antonio_Marín_Guzmán, José; Erker, Paul; Gasparinetti, Simone; Huber, Marcus; Yunger_Halpern, Nicole (November 2024, Reports on Progress in Physics)

   Abstract Controlled quantum machines have matured significantly. A natural next step is to increasingly grant them autonomy, freeing them from time-dependent external control. For example, autonomy could pare down the classical control wires that heat and decohere quantum circuits; and an autonomous quantum refrigerator recently reset a superconducting qubit to near its ground state, as is necessary before a computation. Which fundamental conditions are necessary for realizing useful autonomous quantum machines? Inspired by recent quantum thermodynamics and chemistry, we posit conditions analogous to DiVincenzo’s criteria for quantum computing. Furthermore, we illustrate the criteria with multiple autonomous quantum machines (refrigerators, circuits, clocks, etc) and multiple candidate platforms (neutral atoms, molecules, superconducting qubits, etc). Our criteria are intended to foment and guide the development of useful autonomous quantum machines. 

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3. Extending MAPE-K to support human-machine teaming

   https://doi.org/10.1145/3524844.3528054  

   Cleland-Huang, Jane; Agrawal, Ankit; Vierhauser, Michael; Murphy, Michael; Prieto, Mike (May 2022, Proceedings of the International Symposium on Software Engineering for Adaptive and Self-Managing Systems)

   Schmerl, Bradley R.; Maggio, Martina; Camara, Javier (Ed.)

   The MAPE-K feedback loop has been established as the primary reference model for self-adaptive and autonomous systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems. At the same time, the Human Machine Teaming (HMT) paradigm is designed to promote partnerships between humans and autonomous machines. It goes far beyond the degree of collaboration expected in human-on-the-loop and human-in-the-loop systems and emphasizes interactions, partnership, and teamwork between humans and machines. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions between humans and machines as intended by HMT. In this paper, we present the MAPE-K-HMT framework which augments the traditional MAPE-K loop with support for HMT. We identify critical human-machine teaming factors and describe the infrastructure needed across the various phases of the MAPE-K loop in order to effectively support HMT. This includes runtime models that are constructed and populated dynamically across monitoring, analysis, planning, and execution phases to support human-machine partnerships. We illustrate MAPE-KHMT using examples from an autonomous multi-UAV emergency response system, and present guidelines for integrating HMT into MAPE-K. 

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4. Human-Machine Teaming with small Unmanned Aerial Systems in a MAPE-K Environment

   https://doi.org/10.1145/3618001  

   Cleland-Huang, Jane; Chambers, Theodore; Zudaire, Sebastian; Chowdhury, Muhammed Tawfiq; Agrawal, Ankit; Vierhauser, Michael (September 2023, ACM Transactions on Autonomous and Adaptive Systems)

   ACM (Ed.)

   The Human Machine Teaming (HMT) paradigm focuses on supporting partnerships between humans and autonomous machines. HMT describes requirements for transparency, augmented cognition, and coordination that enable far richer partnerships than those found in typical human-on-the-loop and human-in-the-loop systems. Autonomous, self-adaptive systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems, are often implemented using the MAPE-K feedback loop as the primary reference model. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions that occur between humans and autonomous machines as intended by HMT. In this paper, we, therefore, present the MAPE-K HMT framework which utilizes runtime models to augment the monitoring, analysis, planning, and execution phases of the MAPE-K loop in order to support HMT despite the different operational cadences of humans and machines. We draw on examples from our own emergency response system of interactive, autonomous, small unmanned aerial systems to illustrate the application of MAPE-K HMT in both a simulated and physical environment, and discuss how the various HMT models are connected and can be integrated into a MAPE-K solution. 

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5. Revealing CNN Architectures via Side-Channel Analysis in Dataflow-based Inference Accelerators

   https://doi.org/10.1145/3688001  

   Weerasena, Hansika; Mishra, Prabhat (November 2024, ACM Transactions on Embedded Computing Systems)

   Convolutional Neural Networks (CNNs) are widely used in various domains, including image recognition, medical diagnosis and autonomous driving. Recent advances in dataflow-based CNN accelerators have enabled CNN inference in resource-constrained edge devices. These dataflow accelerators utilize inherent data reuse of convolution layers to process CNN models efficiently. Concealing the architecture of CNN models is critical for privacy and security. This article evaluates memory-based side-channel information to recover CNN architectures from dataflow-based CNN inference accelerators. The proposed attack exploits spatial and temporal data reuse of the dataflow mapping on CNN accelerators and architectural hints to recover the structure of CNN models. Experimental results demonstrate that our proposed side-channel attack can recover the structures of popular CNN models, namely, Lenet, Alexnet, VGGnet16, and YOLOv2. 

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