The advent of massively open online courses (MOOCs) poses several technical challenges for educators. One of these challenges is the need to automate, as much as possible, the generation of problems, creation of solutions, and grading, in order to deal with the huge number of students. We collectively refer to this challenge as automated exercise generation. In this paper, we present a step towards tackling this challenge for an embedded systems course. We present a template-based approach to classifying problems in a recent textbook by Lee and Seshia, and outline approaches to problem and solution generation based on mutation and satisfiability solving. Several directions for future work are also outlined.

As systems-on-chip increase in complexity, the underlying technology presents us with significant challenges due to increased power consumption as well as decreased reliability. Today, designers must consider building systems that achieve the requisite functionality and performance using components that may be unreliable. In order to do so, it is crucial to understand the close interplay between the different layers of a system: technology, platform, and application. This will enable the most general tradeoff exploration, reaping the most benefits in power, performance and reliability. This paper surveys various cross layer techniques and approaches for power, performance, and reliability tradeoffs are technology, circuit, architecture and application layers. © 2013 Elsevier B.V. All rights reserved.