2026 — NOW
Cryogenic GPU Systems
Building a reproducible chain from evidence and parameter synthesis to GPU tracing, simulation, and design-space exploration at 77 K.
RESEARCH / 77 K
From neural-rendering accelerators to cryogenic GPU systems.
My current work studies how device- and system-level changes at 77 K reshape GPU resource allocation and AI workloads. Earlier neural-rendering work remains the methodological origin, not the current destination.
Current question
Where should the cryogenic dividend go?
The ongoing work follows an evidence-first chain: normalize cryogenic measurements, synthesize bounded parameters, characterize representative workloads, and explore architecture choices in reproducible simulation. No unpublished results are disclosed here.
Trajectory
2024 — 2026
GPU architecture and systems
Expanding from individual neural-rendering accelerators toward workloads, memory hierarchies, scheduling, and system modeling.
2022 — 2025
Neural-rendering acceleration
Research spanning algorithm analysis and hardware design for ray representation, sampling, data reuse, and conflict-free encoding.
PUBLICATIONS
Selected publications
Existing publications document the earlier neural-rendering and accelerator phase.
20262026202520242024202420242023
An Energy-Efficient Edge Coprocessor for Neural Rendering With Explicit Data Reuse Strategies
B. Yuan, X. Zhang, Z. Zheng, Y. Zhang, H. Wan, Z. Yuan, J. Chen, Yunxiang He, J. Ding, X. Zhang, C. Rao, W. Su, P. Zhou, J. Yu, X. Lou
SCAR: A Neural Rendering Accelerator with Sparse-Aware Sampling and Conflict-Free Encoding
Yunxiang He, Yongzhi Zhang, Quanyu Chen, Chaofan Li, Qihan Ding, Xin Lou
A Neural Rendering Coprocessor With Optimized Ray Representation and Marching
Z. Yuan, B. Yuan, C. Rao, Y. Zhu, Yunxiang He, P. Zhou, J. Yu, X. Lou
Density Estimation-based Effective Sampling Strategy for Neural Rendering
Yunxiang He, X. Lou
A 0.59μJ/pixel High-throughput Energy-efficient Neural Volume Rendering Accelerator on FPGA
Z. Yuan, B. Yuan, Y. Gu, Y. Zheng, Yunxiang He, X. Wang, C. Rao, P. Zhou, J. Yu, X. Lou
An Efficient Hardware Volume Renderer for Convolutional Neural Radiance Fields
X. Wang, Yunxiang He, X. Zhang, P. Zhou, X. Lou
Ray Reordering for Hardware-Accelerated Neural Volume Rendering
J. Ding, Yunxiang He, B. Yuan, Z. Yuan, P. Zhou, J. Yu, X. Lou
Analysis and Design of Precision-scalable Computation Array for Efficient Neural Radiance Field Rendering
K. Long, C. Rao, Yunxiang He, Z. Yuan, P. Zhou, J. Yu, X. Lou