232 citations · 411 across the 23 of their papers we have counts for
7 papers · 1 filter
COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs
Ruobing Han, Jaewon Lee, Jaewoong Sim +1
As CUDA programs become the de facto program among data parallel applications such as high-performance computing or machine learning applications, running CUDA on other platforms h…
Vortex: Extending the RISC-V ISA for GPGPU and 3D-GraphicsResearch
Blaise Tine, Fares Elsabbagh, Krishna Yalamarthy +1
The importance of open-source hardware and software has been increasing. However, despite GPUs being one of the more popular accelerators across various applications, there is very…
RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU
Geonhwa Jeong, Eric Qin, Ananda Samajdar +4
As AI-based applications become pervasive, CPU vendors are starting to incorporate matrix engines within the datapath to boost efficiency. Systolic arrays have been the premier arc…
Supporting CUDA for an extended RISC-V GPU architecture
Ruobing Han, Blaise Tine, Jaewon Lee +2
With the rapid development of scientific computation, more and more researchers and developers are committed to implementing various workloads/operations on different devices. Amon…
Context-Aware Task Handling in Resource-Constrained Robots with Virtualization
Ramyad Hadidi, Nima Shoghi Ghalehshahi, Bahar Asgari +1
Intelligent mobile robots are critical in several scenarios. However, as their computational resources are limited, mobile robots struggle to handle several tasks concurrently and…
Creating Robust Deep Neural Networks With Coded Distributed Computing for IoT Systems
Ramyad Hadidi, Jiashen Cao, Hyesoon Kim
The increasing interest in serverless computation and ubiquitous wireless networks has led to numerous connected devices in our surroundings. Among such devices, IoT devices have a…
THIA: Accelerating Video Analytics using Early Inference and Fine-Grained Query Planning
Jiashen Cao, Ramyad Hadidi, Joy Arulraj +1
To efficiently process visual data at scale, researchers have proposed two techniques for lowering the computational overhead associated with the underlying deep learning models. T…