Related papers: Teaching Experiences using the RVfpga Package
Various hardware accelerators have been developed for energy-efficient and real-time inference of neural networks on edge devices. However, most training is done on high-performance GPUs or servers, and the huge memory and computing costs…
With the emerging big data applications of Machine Learning, Speech Recognition, Artificial Intelligence, and DNA Sequencing in recent years, computer architecture research communities are facing the explosive scale of various data…
Conceptual modeling has been an important part of constructionist educational practices for many years, particularly in STEM (Science, Technology, Engineering and Mathematics) disciplines. What is not so common is using agent-based…
With the increasing popularity of accelerator technologies (e.g., GPUs and TPUs) and the emergence of domain-specific computing via ASICs and FPGA, the matter of heterogeneity and understanding its ramifications on the performance has…
Most university curricula consider software processes to be on the fringes of software engineering (SE). Students are told there exists a plethora of software processes ranging from RUP over V-shaped processes to agile methods. Furthermore,…
FREESS is a free, interactive simulator that illustrates instruction-level parallelism in a RISC-V-inspired superscalar processor. Based on an extended version of Tomasulo's algorithm, FREESS is intended as a hands-on educational tool for…
With the rapid progress in virtual reality (VR) technology, the scope of VR applications has greatly expanded across various domains. However, the superiority of VR training over traditional methods and its impact on learning efficacy are…
The European Union's technological sovereignty strategy centers around the RISC-V Instruction Set Architecture, with the European Processor Initiative leading efforts to build production-ready processors. Focusing on realizing a functional…
Processing-In-Memory (PIM) architectures offer a promising approach to accelerate Graph Neural Network (GNN) training and inference. However, various PIM devices such as ReRAM, FeFET, PCM, MRAM, and SRAM exist, with each device offering…
Virtual learning environments are a useful modality for engaging students in the classroom by affording them a sense of presence and immersion. The motivation of this project was to create an open-source augmented reality electrical circuit…
Embedded field programmable gate array (eFPGA) technology allows the implementation of reconfigurable logic within the design of an application-specific integrated circuit (ASIC). This approach offers the low power and efficiency of an ASIC…
An operating system project suitable for undergraduate computing/electrical sciences students is presented. The project can be used as a course project in a one semester course, or as a self-study project for motivated students. The course…
This paper outlines an FPGA VLSI design methodology that was used to realize a fully functioning FPGA chip in 130nm CMOS with improved routability and memory robustness. The architectural design space exploration and synthesis capability…
Nowadays, various memory-hungry applications like machine learning algorithms are knocking "the memory wall". Toward this, emerging memories featuring computational capacity are foreseen as a promising solution that performs data process…
Educational Escape Rooms (EER's), through their use of immersive storytelling and practical application of abstract concepts, present a novel new technique for engaging learners in a variety of subjects. However, there is a significant time…
Virtual Platforms (VPs) enable early software validation of autonomous systems' electronics, reducing costs and time-to-market. While many VPs support both functional and non-functional simulation (e.g., timing, power), they lack the…
Generation and exploration of approximate circuits and accelerators has been a prominent research domain to achieve energy-efficiency and/or performance improvements. This research has predominantly focused on ASICs, while not achieving…
Compared to the first generation of deep neural networks, dominated by regular, compute-intensive kernels such as matrix multiplications (MatMuls) and convolutions, modern decoder-based transformers interleave attention, normalization, and…
Use of edge computing in application of Computer Vision (CV) is an active field of research. Today, most CV applications make use of Convolutional Neural Networks (CNNs) to inference on and interpret video data. These edge devices are…
We present the Virtual Quantum Device (VQD) platform, a system based on the QuEST quantum emulator. Through the use of VQDs, non-expert users can emulate specific quantum computers with detailed error models, bespoke gate sets and…