SystemC-based Minimum Intrusive Fault Injection Technique with Improved Fault Representation

In this paper, we propose a new SystemC-based fault injection technique that has improved fault representation in visible and on-the-fly data and signal registers. The technique is minimum intrusive since it only requires replacing the original data or signal types to fault injection enabler types. We compare the proposed simulation technique with recently reported SystemC-based techniques and show that our technique has fast simulation speed, better fault representation, while maintaining simplicity and minimum intrusion. We demonstrate fault injection capabilities in a behavioural SystemC description of MPEG-2 decoder using proposed technique and show that up to 98.9% fault representation within data and signal registers can be achieved

On The Non-linear Distortion Effects in an OFDM-RoF Link

Radio over Fiber (RoF) system is a promising technique for microcell and picocell applications for deployment of future ubiquitous wireless data networks. However, the performance of RoF systems can be severely degraded due to non-linear effects in the channel. Also, Orthogonal Frequency Division Multiplexing (OFDM), as a standard for broadband wireless networks, is being proposed for deployment with RoF systems to facilitate the total performance of a system. In this research, at first, the performance of OFDM-based RoF link with Mach-Zehnder modulator distortion effects has been analyzed at 5.8 GHz. Evaluation of mean-squared error of the proposed OFDM-RoF system was carried out to compare with the conventional single carrier system based RoF link after the modulator distortion case and also for fixed Signal to Noise Ratio (SNR) of 20 dB using undistorted OFDM signal. Later, nominal and offset biasing pre-distortion techniques are applied in proposed system to linearize the OFDM-RoF link. Thus, finally a comparison between the aforementioned pre-distortion techniques applied showed important observation in terms of distortion-free dynamic range and SNR to choose offset pre-distortion technique for our proposed system

On the error vector magnitude as a performance metric and comparative analysis

In this paper, we present the error vector magnitude (EVM) as a a figure of merit for assessing the quality of digitally modulated telecommunication signals. We define EVM for a common industry standard and derive the relationships among EVM, signal to noise ratio (SNR) and bit error rate (BER). We also compare among the different performance metrics and show that EVM can be equivalently useful as signal to noise ratio and bit error rate. A few simulation results are presented to illustrate the performance of EVM based on these relationships

Adaptive energy minimization of OpenMP parallel applications on many-core systems

Energy minimization of parallel applications is an emerging challenge for current and future generations of many-core computing systems. In this paper, we propose a novel and scalable energy minimization approach that suitably applies DVFS in the sequential part and jointly considers DVFS and dynamic core allocations in the parallel part. Fundamental to this approach is an iterative learning based control algorithm that adapt the voltage/frequency scaling and core allocations dynamically based on workload predictions and is guided by the CPU performance counters at regular intervals. The adaptation is facilitated through performance annotations in the application codes, defined in a modified OpenMP runtime library. The proposed approach is validated on an Intel Xeon E5-2630 platform with up to 24 CPUs running NAS parallel benchmark applications. We show that our proposed approach can effectively adapt to different architecture and core allocations and minimize energy consumption by up to 17% compared to the existing approaches for a given performance requirement

Learning-based runtime management of energy-efficient and reliable many-core systems

This paper highlights and demonstrates our research works to date addressing the energy-efficiency and reliability challenges of many-core systems through intelligent runtime management algorithms. The algorithms are implemented through cross-layer interactions between the three layers: application, runtime and hardware, forming our core theme of working together. The annotated application tasks communicate the performance, energy or reliability requirements to the runtime. With such requirements, the runtime exercises the hardware through various control knobs and gets the feedback of these controls through the performance monitors. The aim is to learn the best possible hardware controls during runtime to achieve energy-efficiency and improved reliability, while meeting the specified application requirements

Cyclostationary Random Number Sequences for the Tsetlin Machine

Author's accepted manuscriptThe Tsetlin Machine (TM) constitutes an emerging machine learning algorithm that has shown competitive performance on several benchmarks. The underlying concept of the TM is propositional logic determined by a group of finite state machines that learns patterns. Thus, TM-based systems naturally lend themselves to low-power operation when implemented in hardware for micro-edge Internet-of-Things applications. An important aspect of the learning phase of TMs is stochasticity. For low-power integrated circuit implementations the random number generation must be carried out efficiently. In this paper, we explore the application of pre-generated cyclostationary random number sequences for TMs. Through experiments on two machine learning problems, i.e., Binary Iris and Noisy XOR, we demonstrate that the accuracy is on par with standard TM. We show that through exploratory simulations the required length of the sequences that meets the conflicting tradeoffs can be suitably identified. Furthermore, the TMs achieve robust performance against reduced resolution of the random numbers. Finally, we show that maximum-length sequences implemented by linear feedback shift registers are suitable for generating the required random numbers.acceptedVersio