Biblio

Modern embedded systems need to cater for several needs depending upon the application domain in which they are deployed. For example, mixed-critically needs to be considered for real-time and safety-critical systems and energy for battery-operated systems. At the same time, many of these systems demand for their reliability and security as well. With electronic systems being used for increasingly varying type of applications, novel challenges have emerged. For example, with the use of embedded systems in increasingly complex applications that execute tasks with varying priorities, mixed-criticality systems present unique challenges to designing reliable systems. The large design space involved in implementing cross-layer reliability in heterogeneous systems, particularly for mixed-critical systems, poses new research problems. Further, malicious security attacks on these systems pose additional extraordinary challenges in the system design. In this paper, we cover both the industry and academia perspectives of the challenges posed by these emergent aspects of system design towards designing highperformance, energy-efficient, reliable and/or secure embedded systems. We also provide our views on paths forward.

In recent years, with the rapid development of DNN, the algorithm complexity in a series of fields such as computer vision and natural language processing is increasing rapidly. FPGA-based DNN accelerators have demonstrated superior flexibility and performance, with higher energy efficiency compared to high-performance devices such as GPU. However, the computing resources of a single FPGA are limited and it is difficult to flexibly meet the requirements of high throughput and high energy efficiency of different computing scales. Therefore, this paper proposes a DNN implementation method based on the scalable heterogeneous FPGA cluster to adapt to different tasks and achieve high throughput and energy efficiency. Firstly, the method divides a single enormous task into multiple modules and running each module on different FPGA as the pipeline structure between multiple boards. Secondly, a task deployment method based on dichotomy is proposed to maximize the balance of task execution time of different pipeline stages to improve throughput and energy efficiency. Thirdly, optimize DNN computing module according to the relationship between computing power and bandwidth, and improve energy efficiency by reducing waste of ineffective resources and improving resource utilization. The experiment results on Alexnet and VGG-16 demonstrate that we use Zynq 7035 cluster can at most achieves ×25.23 energy efficiency of optimized AMD AIO processor. Compared with previous works of single FPGA and FPGA cluster, the energy efficiency is improved by 59.5% and 18.8%, respectively.

In this paper, we investigate the protection mechanism of source location privacy, in which back-tracing attack is performed by an adversary. A dynamic and disjoint routing mechanism (DDRM) is proposed to achieve a strong protection for source location privacy in an energy-efficient manner. Specially, the selection of intermediate node renders the message transmission randomly and flexibly. By constructing k disjoint paths, an adversary could not receive sufficient messages to locate the source node. Simulation results illustrate the effectiveness of the proposed mechanism.

There is an ongoing search for finding energy-efficient solutions in multi-core computing platforms. Approximate computing is one such solution leveraging the forgiving nature of applications to improve the energy efficiency at different layers of the computing platform ranging from applications to hardware. We are interested in understanding the benefits of approximate computing in the realm of Apache Hadoop and its applications. A few mechanisms for introducing approximation in programming models include sampling input data, skipping selective computations, relaxing synchronization, and user-defined quality-levels. We believe that it is straightforward to apply the aforementioned mechanisms to conserve energy in Hadoop clusters as well. The emerging trend of approximate computing motivates us to systematically investigate thermal profiling of approximate computing strategies in this research. In particular, we design a thermal-aware approximate computing framework called tHadoop2, which is an extension of tHadoop proposed by Chavan et al. We investigated the thermal behavior of a MapReduce application called Pi running on Hadoop clusters by varying two input parameters - number of maps and number of sampling points per map. Our profiling results show that Pi exhibits inherent resilience in terms of the number of precision digits present in its value.

The limited battery lifetime and rapidly increasing functionality of portable multimedia devices demand energy-efficient designs. The filters employed mainly in these devices are based on Gaussian smoothing, which is slow and, severely affects the performance. In this paper, we propose a novel energy-efficient approximate 2D Gaussian smoothing filter (2D-GSF) architecture by exploiting "nearest pixel approximation" and rounding-off Gaussian kernel coefficients. The proposed architecture significantly improves Speed-Power-Area-Accuracy (SPAA) metrics in designing energy-efficient filters. The efficacy of the proposed approximate 2D-GSF is demonstrated on real application such as edge detection. The simulation results show 72%, 79% and 76% reduction in area, power and delay, respectively with acceptable 0.4dB loss in PSNR as compared to the well-known approximate 2D-GSF.

Assessing the trustworthiness of sensor data and transmitters of this data is critical for quality assurance. Trust evaluation frameworks utilize data provenance along with the sensed data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop sensor network, provenance information requires a large and variable number of bits in each packet, resulting in high energy dissipation due to the extended period of radio communication. In this paper, we design energy-efficient provenance encoding and construction schemes, which we refer to as Probabilistic Provenance Flow (PPF). Our work demonstrates the feasibility of adapting the Probabilistic Packet Marking (PPM) technique in IP traceback to wireless sensor networks. We design two bit-efficient provenance encoding schemes along with a complementary vanilla scheme. Depending on the network size and bit budget, we select the best method based on mathematical approximations and numerical analysis. We integrate PPF with provenance-based trust frameworks and investigate the trade-off between trustworthiness of data items and transmission overhead. We conduct TOSSIM simulations with realistic wireless links, and perform testbed experiments on 15–20 TelosB motes to demonstrate the effectiveness of PPF. Our results show that the encoding schemes of PPF have identical performance with a low bit budget (∼32-bit), requiring 33% fewer packets and 30% less energy than PPM variants to construct provenance. With a twofold increase in bit budget, PPF with the selected encoding scheme reduces energy consumption by 46–60%.