Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators

The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability stand along with the need for reducing the computational burden on the hardware platforms, i.e. reducing the energy consumption and execution time as well as increasing the efficiency of DNN accelerators. Moreover, the growing demand for specialized DNN accelerators with tailored requirements, particularly for safety-critical applications, necessitates a comprehensive design space exploration to enable the development of efficient and robust accelerators that meet those requirements. Therefore, the trade-off between hardware performance, i.e. area and delay, and the reliability of the DNN accelerator implementation becomes critical and requires tools for analysis. This paper presents a comprehensive methodology for exploring and enabling a holistic assessment of the trilateral impact of quantization on model accuracy, activation fault reliability, and hardware efficiency. A fully automated framework is introduced that is capable of applying various quantization-aware techniques, fault injection, and hardware implementation, thus enabling the measurement of hardware parameters. Moreover, this paper proposes a novel lightweight protection technique integrated within the framework to ensure the dependable deployment of the final systolic-array-based FPGA implementation. The experiments on established benchmarks demonstrate the analysis flow and the profound implications of quantization on reliability, hardware performance, and network accuracy, particularly concerning the transient faults in the network's activations.Comment

makeSense: Simplifying the Integration of Wireless Sensor Networks into Business Processes

A wide gap exists between the state of the art in developing Wireless Sensor Network (WSN) software and current practices concerning the design, execution, and maintenance of business processes. WSN software is most often developed based on low-level OS abstractions, whereas business process development leverages high-level languages and tools. This state of affairs places WSNs at the fringe of industry. The makeSense system addresses this problem by simplifying the integration of WSNs into business processes. Developers use BPMN models extended with WSN-specific constructs to specify the application behavior across both traditional business process execution environments and the WSN itself, which is to be equipped with application-specific software. We compile these models into a high-level intermediate language—also directly usable by WSN developers—and then into OS-specific deployment-ready binaries. Key to this process is the notion of meta-abstraction, which we define to capture fundamental patterns of interaction with and within the WSN. The concrete realization of meta-abstractions is application-specific; developers tailor the system configuration by selecting concrete abstractions out of the existing codebase or by providing their own. Our evaluation of makeSense shows that i) users perceive our approach as a significant advance over the state of the art, providing evidence of the increased developer productivity when using makeSense; ii) in large-scale simulations, our prototype exhibits an acceptable system overhead and good scaling properties, demonstrating the general applicability of makeSense; and, iii) our prototype—including the complete tool-chain and underlying system support—sustains a real-world deployment where estimates by domain specialists indicate the potential for drastic reductions in the total cost of ownership compared to wired and conventional WSN-based solutions

Separation of distributed coordination and control for programming reliable robotics

A robot's code needs to sense the environment, control the hardware, and communicate with other robots. Current programming languages do not provide the necessary hardware platform-independent abstractions, and therefore, developing robot applications require detailed knowledge of signal processing, control, path planning, network protocols, and various platform-specific details. Further, porting applications across hardware platforms becomes tedious. With the aim of separating these hardware dependent and independent concerns, we have developed Koord: a domain specific language for distributed robotics. Koord abstracts platform-specific functions for sensing, communication, and low-level control. Koord makes the platform-independent control and coordination code portable and modularly verifiable. It raises the level of abstraction in programming by providing distributed shared memory for coordination and port interfaces for sensing and control. We have developed the formal executable semantics of Koord in the K framework. With this symbolic execution engine, we can identify proof obligations for gaining high assurance from Koord applications. Koord is deployed on CyPhyHouse---a toolchain that aims to provide programming, debugging, and deployment benefits for distributed mobile robotic applications. The modular, platform-independent middleware of CyPhyHouse implements these functionalities using standard algorithms for path planning (RRT), control (MPC), mutual exclusion, etc. A high-fidelity, scalable, multi-threaded simulator for Koord applications is developed to simulate the same application code for dozens of heterogeneous agents. The same compiled code can also be deployed on heterogeneous mobile platforms. This thesis outlines the design, implementation and formalization of the Koord language and the main components of CyPhyHouse that it is deployed on

A Computational Field Framework for Collaborative Task Execution in Volunteer Clouds

The increasing diffusion of cloud technologies is opening new opportunities for distributed and collaborative computing. Volunteer clouds are a prominent example, where participants join and leave the platform and collaborate by sharing their computational resources. The high dynamism and unpredictability of such scenarios call for decentralized self-* approaches to guarantee QoS. We present a simulation framework for collaborative task execution in volunteer clouds and propose one concrete instance based on Ant Colony Optimization, which is validated through a set of simulation experiments based on Google workload data

Practical and Robust Power Management for Wireless Sensor Networks

Wireless Sensor Networks: WSNs) consist of tens or hundreds of small, inexpensive computers equipped with sensors and wireless communication capabilities. Because WSNs can be deployed without fixed infrastructure, they promise to enable sensing applications in environments where installing such infrastructure is not feasible. However, the lack of fixed infrastructure also presents a key challenge for application developers: sensor nodes must often operate for months or years at a time from fixed or limited energy sources. The focus of this dissertation is on reusable power management techniques designed to facilitate sensor network developers in achieving their systems\u27 required lifetimes. Broadly speaking, power management techniques fall into two categories. Many power management protocols developed within the WSN community target specific hardware subsystems in isolation, such as sensor or radio hardware. The first part of this dissertation describes the Adaptive and Robust Topology control protocol: ART), a representative hardware-specific technique for conserving energy used by packet transmissions. In addition to these single-subsystem approaches, many applications can benefit greatly from holistic power management techniques that jointly consider the sensing, computation, and communication costs of potential application configurations. The second part of this dissertation extends this holistic power management approach to two families of structural health monitoring applications. By applying a partially-decentralized architecture, the cost of collecting vibration data for analysis at a centralized base station is greatly reduced. Finally, the last part of this dissertation discusses work toward a system for clinical early warning and intervention. The feasibility of this approach is demonstrated through preliminary study of an early warning component based on historical clinical data. An ongoing clinical trial of a real-time monitoring component also provides important guidelines for future clinical deployments based on WSNs