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Found 6 papers, 3 papers with code
MoE-Gen: High-Throughput MoE Inference on a Single GPU with Module-Based Batching
This paper presents MoE-Gen, a high-throughput MoE inference system optimized for single-GPU execution.
MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
Its key innovation is a sparsity-aware CAP analysis model, the first to integrate cost, performance, and accuracy metrics into a single diagram while estimating the impact of sparsity on system performance.
Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo
Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods.
MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache
This paper presents MoE-Infinity, an efficient MoE inference system designed for personal machines with limited GPU memory capacity.
Pano-NeRF: Synthesizing High Dynamic Range Novel Views with Geometry from Sparse Low Dynamic Range Panoramic Images
However, in the case of inputting sparse Low Dynamic Range (LDR) panoramic images, NeRF often degrades with under-constrained geometry and is unable to reconstruct HDR radiance from LDR inputs.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
