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The increasing complexity and connectivity of power systems is making it increasingly likely that they will be subject to malicious attacks that compromise operation. Recent studies have shown that these systems are vulnerable to a wide range of cyber-attacks, including False Data Injection (FDI). Conventional security monitoring and protection tools are based on passive defense strategies. In this paper, we propose an approach for active defense that improves system security and the FDI attack detection rate. The key insight for this approach is that emerging micro-grids can utilize distributed energy resources to dynamically reconfigure the system (e.g. current flow paths), across multiple acceptable configurations. Instead of using information from only a single configuration to detect FDI attacks, our proposed approach uses dynamic reconfiguration to compare measured and estimated states under multiple configurations to accurately detect FDI attacks. We evaluate our approach in the specific scenario of emerging micro-grids. We develop a novel technique for state estimation using multiple configurations and demonstrate that this approach significantly improves FDI detection accuracy.

With medical devices and Internet-of-Things (IoT) becoming increasingly connected, e-health applications have been widely developing. However, security of both sensitive health information and critical device settings is a significant concern. Due to the intrinsic limitations of many medical devices, including low power and limited computational resources, security and device performance can be difficult to balance - especially for implanted devices, as implanted devices require a surgical procedure to replace the device or battery. In this work we propose a system model for e-health applications with consideration of heterogeneous medical devices, in terms of battery capacity and computational ability. In addition, different attacks are considered in the proposed system model. A case study is presented to show that the proposed system model can support heterogeneous medical devices with varying power and resource constraints. The case study demonstrates that it is possible to significantly reduce the overhead for security on power-constrained devices based on the proposed system model.

This research full paper explores the magnitude of added course fees and miscellaneous costs in an engineering program and how these fees impact lower socio-economic students. Previous work has examined the effects of added course fees and development boards on students from various socioeconomic backgrounds. While the study found that students of varying socioeconomic status did not agree that they felt "ostracized" as a result of added course expenses, the study did not examine whether students felt other, less severe feelings of exclusion or loss of identity in relation to engineering costs.This paper provides an in-depth exploration of the effects of added course expenses on students’ sense of inclusion in Electrical and Computer Engineering Programs. Specific emphasis is placed on how student experience varies by socio-economic status, and the experience of low-socio-economic status is treated with particular interest. "Added course expenses" includes any development boards, parts, and personal computing resources (laptops) necessary for student success but not directly charged by the school for enrollment in a course.

Large wildfires can cause significant damages to communities and ecosystems. In California in 2018, 87 people were killed, 2 billion acres were burned, and $16.5 billion in costs were incurred by the State. With increased drought and weather variability, this problem is likely only to worsen. In this paper we propose an approach for detecting early onset fires by leveraging equipment already deployed in existing power grid infrastructures, including those that cover remote areas. Our approach is based on the observation that radiated heat from fires quickly results in changes in transmission line temperatures and sag. As the transmission line temperature increase from radiated heat from a fire, the line also expands in length resulting in sag. By monitoring line temperatures and sag, we show that early onset wildfires (e.g. a 50 m2 wildfire) can be detected in less than a minute and up to 120m away from the transmission line.

Energy becomes an inevitable challenge when using a large die-stacking dynamic random access memory (DRAM) cache. Although emerging spin-transfer-torque-RAM (STT-RAM) technology can efficiently reduce the static energy of large cache, it cannot completely replace DRAM cache due to the high write energy of STT-RAM. Recently, researchers have observed that there are many redundant bits written in the row buffer and futile bits written back to STT-RAM cells, which do not change the cells’ value but still cost high write energy. In this paper, we first design a large hybrid cache architecture with the DRAM region and the STT-RAM region to reduce the high static energy of DRAM cache. The selective write back to row buffer and selective write back to cell array optimizations are proposed to reduce high write energy of the STT-RAM region by removing the unnecessary bit-writes. Furthermore, we propose reuse distance-oriented data movement to reduce write operations in the STT-RAM region. Finally, we propose a novel tag design for the hybrid cache by moving all tag arrays to the STT-RAM region. The SPEC CPU2006 benchmarks show an average 28.3% energy reduction and 6.7% performance improvement for the write optimizations and 7.3% energy savings and 27.5% instructions per cycle speedups for the proposed tag design.

This abstract is for a Full Paper in the category of Innovative Practice. In Electrical and Computer Engineering programs, we are increasingly seeing a digital divide between students who can study and work on assignments 24/7 from powerful laptops, and those students who are forced to rely on the college’s fixed, limited access computer labs to succeed. This paper discusses a proposal to address this problem in a low-to-no cost way at California Polytechnic State University by issuing underprivileged students laptops that have been recently retired from faculty use. The paper looks at the cost implications (and opportunities) of performing a light refurbishment on old faculty units, and provides benchmarks of retired faculty machines against newer faculty laptops across a number of common Electrical Engineering computation tasks.

Power system cyber-attacks today have become a significant design concern. By initiating false commands and/or injecting false data into power grid controls, attackers can perturb a variety of system state and dynamics. With a rapidly evolving energy landscape and aggressive carbon reduction goals in many locations [1], distributed energy storage systems are likely to be broadly deployed in the future. Energy storage can be used for balancing power demands and managing increased variability from solar and wind resources, as well as increasingly widespread electric vehicle charging stations. In this paper, we study the impact of attacks on emerging power grids with widespread energy storage. Based on a theoretical analysis and numerical simulations, we find that grids using widespread storage can lead to increased system vulnerabilities if not managed intelligently. Finally, we propose an approach for managing storage such that the overall grid is more resilient to attacks.

As power grid control systems become increasingly automated and distributed, security has become a significant design concern. Systems increasingly expose new avenues, at a variety of levels, for attackers to exploit and enable widespread disruptions and/or surveillance. Much prior work has explored the implications of attack models focused on false data injection at the front-end of the control system (i.e. during state estimation) [1]. Instead, in this paper we focus on characterizing the inherent cyber-attack vulnerabilities with power flow. Power flow (and power flow constraints) are at the core of many applications critical to operation of power grids (e.g. state estimation, economic dispatch, contingency analysis, etc.). We propose two algorithmic approaches for characterizing the vulnerability of buses within power grids to cyber-attacks. Specifically, we focus on measuring the instability of power flow to attacks which manifest as either voltage or power related errors. Our results show that attacks manifesting as voltage errors are an order of magnitude more likely to cause instability than attacks manifesting as power related errors (and 5x more likely for state estimation as compared to power flow).

This Research Work-In-Progress Paper explores potential causes of measured differences in mental health between students in a joint Computer Engineering Program and students in the two home departments (Electrical Engineering and Computer Science). California Polytechnic State University (Cal Poly) currently runs its Computer Engineering Program (CPE) as a joint offering of the Electrical Engineering (EE) and Computer Science and Software Engineering (CSSE) Departments. The curriculum for this program is made up of roughly 50% computer science courses and 50% electrical engineering courses. With the exception of a single senior level capstone course, all CPE courses are also available to (and in many cases required for) students from the two home departments. As a result of this blended curriculum, it has long been assumed that CPE students would have a similar experience and a similar level of mental wellness to their peers in EE and CSSE. A recent study conducted at Cal Poly, however, indicates that CPE students are substantially more likely to screen positive for risk of Serious Mental Illness (SMI) than their peers. This study uses qualitative interviews with CPE, EE, and CSSE students try to determine potential stressors unique to each program. This study also delves into the quantitative survey data to look for population specific trends that may be feeding into this result.

Continuous pursuit of higher performance and energy efficiency has led to heterogeneous SoC that contains multiple dedicated hardware accelerators. These accelerators exploit the inherent parallelism of tasks and are often tolerant to inaccuracies in their outputs, e.g. image and digital signal processing applications. At the same time, permanent faults are escalating due to process scaling and power restrictions, leading to erroneous outputs. To address this issue, in this paper, we propose a low-cost, universal fault-recovery/repair method that utilizes supervised machine learning techniques to ameliorate the effect of permanent fault(s) in hardware accelerators that can tolerate inexact outputs. The proposed compensation model does not require any information about the accelerator and is highly scalable with low area overhead. Experimental results show, the proposed method improves the accuracy by 50% and decreases the overall mean error rate by 90% with an area overhead of 5% compared to execution without fault compensation.

Energy becomes an inevitable challenge when using a large die-stacking DRAM cache as part of memory. Emerging volatile STT-RAM can be integrated with DRAM as a software-managed hybrid cache to effectively reduce the static and dynamic energy of large cache, but there is extra refresh energy overhead. We observe that reducing the refresh rate of volatile STT-RAM will provide significant energy savings while introducing a small number of bit errors that can be easily tolerated by most error-resilient applications. Thus, we propose a quality-aware approximate die-stacking hybrid cache and develop a novel data allocation scheme. We also propose the online quality monitor and the light-weight check scheme for error recovery. The results show an average 91% reduction in volatile STT-RAM refresh energy with minimal loss in output quality.

Energy is quickly becoming an inevitable challenge to using a large die-stacking DRAM cache. Emerging STT-RAM technology can efficiently reduce the static energy of large cache. However, STT-RAM which has high write energy and latency is not suitable to completely substitute for on-die DRAM cache. We observe that there are a large number of redundant bits written in the row buffer and futile bits written back to STT-RAM cells, which do not actually change the cells' value but still cost write energy. We utilize this opportunity to reduce energy by removing the unnecessary bit-writes to the STT-RAM cache. In this paper, we design a large hybrid cache architecture with a DRAM region and STT-RAM region. Selective Write Back to Row Buffer and Selective Write Back to Cell Array optimizations are proposed to reduce high write energy of STT-RAM region by comparing write data and existing data in advance. Also, we propose Reuse Distance Oriented Data Movement to reduce write operations in STT-RAM region. The results show our hybrid cache can achieve up to a 28.3% energy reduction and 6.7% performance improvement.

With process scaling and the adoption of post-CMOS technologies, permanent faults are becoming a fundamental problem. Circuits containing defects are either discarded (reducing yield) or partially disabled (reducing performance). In this paper, we propose a general approach using supervised and discriminative learning techniques to compensate for the effect of permanent faults on a circuit's output. The insight for this approach is that many emerging systems and applications are able to tolerate some loss of quality in their computed results. Therefore, more scalable and lower overhead compensation techniques may be used to approximately correct for the effect of hardware faults on the circuit output. The proposed approach is shown to improve the output quality of complex accelerator and application-specific logic by 2-3 orders of magnitude while incurring

With process scaling and the adoption of post-cmos technologies, reliability and power are becoming a significant concern for future computing systems, especially highly parallel systems. Previous approaches have investigated augmenting applications with additional logic to detect and correct errors efficiently. In this research, we investigate the impact of different algorithmic designs on error resilience and propose an approach for algorithm selection for a class of equations, i.e. partial differential equations (PDEs), that are at the core of many scientific computing applications, which drive HPC systems. Many different schemes have been devised for the approximation of PDE systems, each with different accuracy, stability, and performance properties. In this research, there are two primary questions that we address: (1) Does numerical stability translate to error resilience? and (2) How do we design schemes to improve error resilience? If an algorithm's error resilience is correlated with its numerical stability properties, this may allow us to design more resilient applications by leveraging well established information on numerical stability. Even with a clear translation of numerical stability to error resilience properties, the question of designing these algorithms still remains however, due to the variety of implementations, schemes, and largely input specific nature of the design. In this research, we propose one approach for automated design using machine-learning. We observe that intelligent selection of the algorithm or a given problem, improves robustness by 20%-50%, on average, over the traditional selection of algorithms, without the addition of any other detection/correction logic.