3,134 research outputs found
A Survey of Techniques for Improving Security of GPUs
Graphics processing unit (GPU), although a powerful performance-booster, also
has many security vulnerabilities. Due to these, the GPU can act as a
safe-haven for stealthy malware and the weakest `link' in the security `chain'.
In this paper, we present a survey of techniques for analyzing and improving
GPU security. We classify the works on key attributes to highlight their
similarities and differences. More than informing users and researchers about
GPU security techniques, this survey aims to increase their awareness about GPU
security vulnerabilities and potential countermeasures
Accelerating Radio Wave Propagation Algorithms by Implementation on Graphics Hardware
Radio wave propagation prediction is a fundamental prerequisite for planning, analysis and optimization of radio networks. For instance coverage analysis, interference estimation or channel and power allocation all rely on propagation predictions. In wireless communication networks optimal antenna sites are determined by either conducting a serie
FPGA Acceleration of Communication-Bound Streaming Applications: Architecture Modeling and a 3D Image Compositing Case Study
Reconfigurable computers usually provide a limited
number of different memory resources, such as host
memory, external memory, and on-chip memory with different
capacities and communication characteristics. A key
challenge for achieving high-performance with reconfigurable
accelerators is the efficient utilization of the available memory
resources. A detailed knowledge of the memories' parameters
is key for generating an optimized communication layout. In this paper, we discuss a benchmarking environment
for generating such a characterization. The environment is
built on IMORC, our architectural template and on-chip
network for creating reconfigurable accelerators. We provide
a characterization of the memory resources available on the
XtremeData XD1000 reconfigurable computer. Based on this
data, we present as a case study the implementation of a 3D
image compositing accelerator that is able to double the frame rate
of a parallel renderer
Neural Rendering and Its Hardware Acceleration: A Review
Neural rendering is a new image and video generation method based on deep
learning. It combines the deep learning model with the physical knowledge of
computer graphics, to obtain a controllable and realistic scene model, and
realize the control of scene attributes such as lighting, camera parameters,
posture and so on. On the one hand, neural rendering can not only make full use
of the advantages of deep learning to accelerate the traditional forward
rendering process, but also provide new solutions for specific tasks such as
inverse rendering and 3D reconstruction. On the other hand, the design of
innovative hardware structures that adapt to the neural rendering pipeline
breaks through the parallel computing and power consumption bottleneck of
existing graphics processors, which is expected to provide important support
for future key areas such as virtual and augmented reality, film and television
creation and digital entertainment, artificial intelligence and the metaverse.
In this paper, we review the technical connotation, main challenges, and
research progress of neural rendering. On this basis, we analyze the common
requirements of neural rendering pipeline for hardware acceleration and the
characteristics of the current hardware acceleration architecture, and then
discuss the design challenges of neural rendering processor architecture.
Finally, the future development trend of neural rendering processor
architecture is prospected
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