A key challenge for self-driving vehicle researchers is to curate massive instrumented vehicle datasets. A common task in their development workflow is to extract video segments that meet particular criteria, such as a particular road scenario or vehicle maneuver. We present a novel approach for detecting vehicle maneuvers from monocular dashboard camera video building upon a deep learning visual odometry model (DeepV2D) to estimate frame-accurate ego-vehicle movement. We leverage image classification and lane line estimation to extend our technique. We classify movement sequences against reference maneuvers using dynamic time warping. We describe, implement, and evaluate classifiers to recognize maneuvers such as turns, lane changes, and deceleration. We show that using deep learning visual odometry to estimate location is superior to consumer-grade high-resolution GPS for this application. We describe and implement a greedy approach to classify maneuvers and evaluate our approach on common road maneuvers. We find an overall AUROC value of 0.91 for turns and 0.84 for all maneuvers in our dataset.

We describe our experience teaching an undergraduate capstone (and elective graduate course) in computer architecture with a semester-long project in which teams of five students design and implement an out-of-order (OoO) pipelined processor core using the open-source RISC-V instruction set. The course content includes OoO scheduling algorithms for instructions to exploit instruction-level parallelism (ILP), example microarchitectures, caching, prefetching, and virtual memory. The labs and projects help students gain proficiency with the SystemVerilog language.

Understanding dynamic program behavior is critical in many stages of the software development lifecycle, for purposes as diverse as optimization, debugging, testing, and security. This paper focuses on the problem of predicting dynamic program behavior statically. We introduce a novel technique to statically identify hot paths that leverages emerging deep learning techniques to take advantage of their ability to learn subtle, complex relationships between sequences of inputs. This approach maps well to the problem of identifying the behavior of sequences of basic blocks in program execution. Our technique is also designed to operate on the compiler's intermediate representation (IR), as opposed to the approaches taken by prior techniques that have focused primarily on source code, giving our approach language-independence. We describe the pitfalls of conventional metrics used for hot path prediction such as accuracy, and motivate the use of Area Under the Receiver Operating Characteristic curve (AUROC). Through a thorough evaluation of our technique on complex applications that include the SPEC CPU2006 benchmarks, we show that our approach achieves an AUROC of 0.85.