Cycle-level GPU simulators are valuable educational tools, but existing frameworks are either too complex for students to navigate or too abstract to convey microarchitectural details. We present Macsim Mini, a lightweight cycle-level GPU simulator designed for computer architecture education. By concentrating on the memory hierarchy and thread scheduling rather than detailed compute pipelines, Macsim Mini captures the architectural trade-offs most central to GPU performance in a codebase small enough for students to read and modify within course assignments. Macsim Mini has been deployed in a graduate-level GPU architecture and programming course for seven semesters, serving $\sim 1,000$ students with high completion rates and average scores above 90%.

Graphics rendering remains one of the most compute-intensive and memory-bound applications of GPUs and has been driving their push for performance and energy efficiency since its inception. Early GPU architectures focused only on accelerating graphics rendering and implemented dedicated a fixed-function rendering units. Today’s GPUs have become more programmable to address the complexity and diversity of modern graphics workloads while still accelerating several components of the graphics pipeline in fixed-function hardware.Generalizing the GPU microarchitecture and implement some of its graphics hardware blocks in software can save area that can be used to expand the generic pipeline, especially in mobile systems-on-chips environments where power and area is scarce.In this work, we propose a RISC-V-based hybrid GPU architecture that accelerates the graphics pipeline without paying the cost of a full hardware graphics pipeline. We evaluated the design on an Altera Arria 10 FPGA running at 200 MHz.

With fully autonomous flight capabilities coupled with user-specific applications, drones, in particular quadcopter drones, are becoming prevalent solutions in myriad commercial and research contexts. However, autonomous drones must operate within constraints and design considerations that are quite different from any other compute-based agent. At any given time, a drone must arbitrate among its limited compute, energy, and electromechanical resources. Despite huge technological advances in this area, each of these problems has been approached in isolation and drone systems design-space tradeoffs are largely unknown. To address this knowledge gap, we formalize the fundamental drone subsystems and find how computations impact this design space. We present a design-space exploration of autonomous drone systems and quantify how we can provide productive solutions. As an example, we study widely used simultaneous localization and mapping (SLAM) on various platforms and demonstrate that optimizing SLAM on FPGA is more fruitful for the drones. Finally, to address the lack of publicly available experimental drones, we release our open-source drone that is customizable across the hardware-software stack.

Fully autonomous drones have a plethora of applications in the real world, from agriculture and communication to public services. With increasing attention, a new market segment has opened up for highly efficient drones. However, the deployment of efficient drones requires an in-depth analysis of several components spanning from hardware sensors to software stack. Specifically, to achieve high reliability, safety, and performance, the top concerns in the professional drone industry are characterizing underlying architecture and flight stack. In this paper, we characterize a widely-used open source flight stack, ArduCopter, to understand the performance requirements as a research community. Additionally, we study how area-specific applications affect flight stack. Our characterizations and benchmarks indicate that the drone flying range can be dramatically increased by optimizing the underlying flight controller software.