Time-Aware Dynamic Binary Instrumentation

The complexity of modern software systems has been rapidly increasing. Program debugging and testing are essential to ensure the correctness of such systems. Program analysis is critical for understanding system’s behavior and analyzing performance. Many program analysis tools use instrumentation to extract required information at run time. Instrumentation naturally alters a program’s timing properties and causes perturbation to the program under analysis. Soft real-time systems must fulfill timing constraints. Missing deadlines in a soft real-time system causes performance degradation. Thus, time-sensitive systems require specialized program analysis tools. Time-aware instrumentation preserves the logical correctness of a program and respects its timing constraints. Current approaches for time-aware instrumentation rely on static source-code instrumentation techniques. While these approaches are sound and effective, the need for running worst-case execution time (WCET) analysis pre- and post-instrumentation reduces the applicability to only hard real-time systems where WCET analysis is common. They become impractical beyond microcontroller code for instrumenting large programs along with all their library dependencies. In this thesis, we introduce theory, method, and tools for time-aware dynamic instrumentation realized in DIME tool. DIME is a time-aware dynamic binary instrumentation framework that adds an adjustable bound on the timing overhead to the program under analysis. DIME also attempts to increase instrumentation coverage by ignoring redundant tracing information. We study parameter tuning of DIME to minimize runtime overhead and maximize instrumentation coverage. Finally, we propose a method and a tool to instrument software systems with quality of service (QoS) requirements. In this case, DIME collects QoS feedback from the system under analysis to respect user-defined performance constraints. As a tool for instrumenting soft real-time applications, DIME is practical, scalable, and supports multi-threaded applications. We present several case studies of DIME instrumenting large and complex applications such as web servers, media players, control applications, and database management systems. DIME limits the instrumentation overhead of dynamic instrumentation while achieving a high instrumentation coverage

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Evaluation of Dynamic Binary Instrumentation Approaches: Dynamic Binary Translation vs. Dynamic Probe Injection

From web browsing to bank transactions, to data analysis and robot automation, just about any task necessitates or benefits from the use of software. Ensuring a piece of software to be effective requires profiling the program’s behavior to evaluate its performance, debugging the program to fix incorrect behaviors, and examining the program to detect security flaws. These tasks are made possible by instrumentation---the method of inserting code into a program to collect data about its behavior. Dynamic binary instrumentation (DBI) enables programmers to understand and reason about program behavior by inserting code into a binary during run time to collect relevant data, and is more flexible than static or source-code instrumentation, but incurs run-time overhead. This thesis attempts to extend the preexisting characterization of the tradeoffs between dynamic binary translation (DBT) and dynamic probe injection (DPI), two popular DBI approaches, using Pin and LiteInst as sample frameworks. It also describes extensions to the LiteInst framework that enable it to instrument function exits correctly. This evaluation involved using the two frameworks to instrument a set of SPEC CPU 2006 benchmarks for counting function entries alone, counting both function entries and exits, or dynamically generating a call graph. On these instrumentation tasks, Pin performed close to native binary time while LiteInst performed significantly slower. Exceptions to this observation, and analysis of the probe types used by LiteInst, suggest that LiteInst incurs significant overhead when executing a large number of probes

MPGemmFI: A Fault Injection Technique for Mixed Precision GEMM in ML Applications

Emerging deep learning workloads urgently need fast general matrix multiplication (GEMM). To meet such demand, one of the critical features of machine-learning-specific accelerators such as NVIDIA Tensor Cores, AMD Matrix Cores, and Google TPUs is the support of mixed-precision enabled GEMM. For DNN models, lower-precision FP data formats and computation offer acceptable correctness but significant performance, area, and memory footprint improvement. While promising, the mixed-precision computation on error resilience remains unexplored. To this end, we develop a fault injection framework that systematically injects fault into the mixed-precision computation results. We investigate how the faults affect the accuracy of machine learning applications. Based on the error resilience characteristics, we offer lightweight error detection and correction solutions that significantly improve the overall model accuracy if the models experience hardware faults. The solutions can be efficiently integrated into the accelerator's pipelines

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Preliminary candidate advanced avionics system for general aviation

An integrated avionics system design was carried out to the level which indicates subsystem function, and the methods of overall system integration. Sufficient detail was included to allow identification of possible system component technologies, and to perform reliability, modularity, maintainability, cost, and risk analysis upon the system design. Retrofit to older aircraft, availability of this system to the single engine two place aircraft, was considered

Integrated Circuit Design for Radiation Sensing and Hardening.

Beyond the 1950s, integrated circuits have been widely used in a number of electronic devices surrounding people’s lives. In addition to computing electronics, scientific and medical equipment have also been undergone a metamorphosis, especially in radiation related fields where compact and precision radiation detection systems for nuclear power plants, positron emission tomography (PET), and radiation hardened by design (RHBD) circuits for space applications fabricated in advanced manufacturing technologies are exposed to the non-negligible probability of soft errors by radiation impact events. The integrated circuit design for radiation measurement equipment not only leads to numerous advantages on size and power consumption, but also raises many challenges regarding the speed and noise to replace conventional design modalities. This thesis presents solutions to front-end receiver designs for radiation sensors as well as an error detection and correction method to microprocessor designs under the condition of soft error occurrence. For the first preamplifier design, a novel technique that enhances the bandwidth and suppresses the input current noise by using two inductors is discussed. With the dual-inductor TIA signal processing configuration, one can reduce the fabrication cost, the area overhead, and the power consumption in a fast readout package. The second front-end receiver is a novel detector capacitance compensation technique by using the Miller effect. The fabricated CSA exhibits minimal variation in the pulse shape as the detector capacitance is increased. Lastly, a modified D flip-flop is discussed that is called Razor-Lite using charge-sharing at internal nodes to provide a compact EDAC design for modern well-balanced processors and RHBD against soft errors by SEE.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111548/1/iykwon_1.pd