In this Research-To-Practice WIP paper, the authors are going to introduce educational modules about green computing and sustainable software development in an elective course, named Algorithms for Big Data, which is taught at the Electrical and Computer Engineering Department hosting this study. The initiative is aiming to educate undergraduate computer engineers about the negative impact of the software industry on the environment and how to design sustainable solutions for different sectors of this industry. The impact of the new modules on students is assessed via a survey and the results show that students find the modules to be interesting and enriched their knowledge about the problem. The authors will also share the best methods of incorporating green computing and sustainable software development in undergraduate engineering curricula.

This work-in-progress research-to-practice paper describes a study of the effects of design problem solving on diversity, equity, and inclusion in Electrical and Computer Engineering classes. Design problems promote design thinking which entails students thinking about how to deploy new knowledge to come up with solutions. In this study, the effect of design problem solving on diversity, equity, and inclusion, is examined as 52 students have faced challenges of design problems in an electrical and computer engineering class.

This work-in-progress research-to-practice paper describes a study of the effects of early introduction of Electrical and Computer Engineering students to design problem solving. Design problems promote design thinking which entails students thinking about how to deploy new knowledge to come up with solutions. The courses in this study are sophomore-level, core courses which entail classical lecturing and analytical problems that only end with a unique solution. Design problem solving modules are introduced to sophomore courses and the student's performance and satisfaction are gaged.

This Research to Practice Full Paper presents a detailed study on the perspectives of students on different online lecture delivery method for programming classes, amid the COVID-19 pandemic. During the period of spring, summer, and fall 2020, a total of 770 students across six different courses, and spanning the freshman, sophomore, and junior years, were surveyed to express their opinions in three different online lecture delivery methods, in the school hosting this study. The survey was designed to capture the main problems and obstacles the student had faced in an online learning environment. The authors analyze the obtained results and recommend best practices to successfully lead programming classes in an online learning environment.

Electronics courses were commonly completed with hardware and software labs to complement teaching electronics theory and enrich the learning experience for students. The COVID-19 pandemic has adversely affected the practical implementation of electronic circuits and the quality of electronics teaching. In this survey-based study, instructors of electronics book courses and labs, during the first year of the pandemic, discuss how to alleviate the limitations of the pandemic on electronics teaching. This study is a work-in-progress.

The COVID-19 pandemic has impeded the education process in many ways. It has been reported that both instructors and students have dreaded the transformation to online platforms and have deemed the emergency classes ineffectual. However, by the end of the first year of the pandemic, when in-person attendance was permitted through hybrid classrooms, poor in-person classroom attendances were commonly reported. In this study, the instructors and students are surveyed to reveal the challenges of the transformation to online classes, and to induce the causes behind the poor in-person attendance of classes during the pandemic. This study is a work in progress.

Neuromorphic computing systems are under heavy investigation as a potential substitute for the traditional von Neumann systems in high-speed low-power applications. Recently, memristor crossbar arrays were utilized in realizing spiking-based neuromorphic system, where memristor conductance values correspond to synaptic weights. Most of these systems are composed of a single crossbar layer, in which system training is done off-chip, using computer based simulations, then the trained weights are pre-programmed to the memristor crossbar array. However, multi-layered, on-chip trained systems become crucial for handling massive amount of data and to overcome the resistance shift that occurs to memristors overtime. In this work, we propose a spiking-based multi-layered neuromorphic computing system capable of online training. The system performance is evaluated using three different datasets showing improved results versus previous work. In addition, studying the system accuracy versus memristor resistance shift shows promising results.

Neuromorphic computing systems are inspired by humans brains, where data are stored and processed at the same location. Contrary to von Neumann systems, neuromorphic computing systems offer excellent real-time processing for huge data sizes, at low costs and power consumption. Most of these systems rely on emerging new devices, such as memristors, to build crossbar arrays implementing different neural network topologies. The Echo State Network model is a special type of recurrent neural networks, which can correctly represent spatiotemporal dataset. In this paper, a new hardware implementation design for the Echo State Network model using memristor double crossbar arrays is proposed. Moreover, a detailed design procedure is proposed for designing and simulating the proposed architecture. The system has been evaluated using the ubiquitous Mackey-Glass dataset showing promising results, compared to the software implementation of the model. In addition, the system shows excellent immunity against memristor process variations.