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Found 8 papers, 2 papers with code
Proteus: Preserving Model Confidentiality during Graph Optimizations
Development of new DL models typically involves two parties: the model developers and performance optimizers.
DISORF: A Distributed Online NeRF Training and Rendering Framework for Mobile Robots
We present a framework, DISORF, to enable online 3D reconstruction and visualization of scenes captured by resource-constrained mobile robots and edge devices.
DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines
In this work, we observe that the gradient computation phase during training is a significant bottleneck on GPUs due to the large number of atomic operations that need to be processed.
ENVIDR: Implicit Differentiable Renderer with Neural Environment Lighting
In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging specular reflections.
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception
We also demonstrate a speedup of up to 1. 6X for inference using CNNs for tasks such as depth estimation, object recognition, and optical flow estimation, with almost no loss in accuracy.
SPIDR: SDF-based Neural Point Fields for Illumination and Deformation
To enable more accurate illumination updates after deformation, we use the shadow mapping technique to approximate the light visibility updates caused by geometry editing.
CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture
In this work, we aim to accelerate inference and training of coordinate-based MLPs for implicit neural representations by proposing a new split MLP architecture, CoordX.
Echo: Compiler-based GPU Memory Footprint Reduction for LSTM RNN Training
For each feature map recomputation to be effective and efficient, its effect on (1) the total memory footprint, and (2) the total execution time has to be carefully estimated.
