arXiv subjects

Xingyang Li

Publications and source records attributed to Xingyang Li.

11 recordsLinked to original sources

arXiv2026

Atlas: Algorithm-Hardware Co-Design for On-Device City-Scale 3D Gaussian Splatting in VR

3D Gaussian splatting (3DGS) has drawn significant attention in the architectural community recently. However, enabling city scale 3DGS on mobile VR devices remains challenging, as the memory requirement of large scale scenes far exceeds the memory capacity of today's mobile GPUs. This paper presents Atlas, an on device city scale 3DGS rendering framework that enables scalable rendering without runtime Internet access. The key insight is that although the full 3DGS model is massive, each frame only requires a small subset of Gaussians under the current pose and level of detail requirement. Based on this insight, Atlas introduces a hierarchical memory offloading mechanism that dynamically loads only necessary Gaussian data into device memory. To further improve performance, Atlas proposes temporal aware LoD search and stereo rasterization to avoid redundant computation in VR. We further show that our technique can be integrated with existing 3DGS accelerators with negligible hardware overhead. Overall, Atlas achieves 18.5x speedup over the GPU baseline and 3.9x speedup over the state of the art 3DGS accelerators, with 92.4% energy savings.

cs.AR↗

arXiv2026

FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models

Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications. However, post-training of large diffusion models is still challenging due to the prohibitive memory footprints and slow training speed, which existing parameter-efficient fine-tuning methods only partially address. To overcome these limitations, we propose FourTune, an efficient post-training framework for diffusion models based on an end-to-end W4A4G4 paradigm. FourTune introduces a triple-branch hybrid pipeline that augments the standard LoRA architecture with a frozen numerical stabilizer to isolate quantization-sensitive outliers, enabling stable training under native 4-bit computation. In addition, FourTune employs hardware-efficient block-wise quantization and customized fused kernels to support efficient quantized backpropagation and reduce memory bandwidth overhead. Across customization, reinforcement learning, and distillation tasks, FourTune matches the quality of full-precision fine-tuning. On FLUX.1-dev (12B), FourTune reduces memory overhead by 2.25$\times$ and increases end-to-end training throughput by 2.27$\times$ compared to BF16 LoRA.

cs.LG↗

arXiv2026

SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training

Long-horizon LLM agents can benefit from reusable skills, yet existing skill-based methods often rely on external skill generators during training or persistent skill retrieval at inference, increasing engineering complexity, context length, and deployment latency. We propose Self-Internalizing Reinforcement learning with Intrinsic skills (SIRI), a three-phase framework that enables agents to discover, validate, and internalize skills without external skill generators or inference-time skill banks. SIRI first warms up the policy with GiGPO to acquire basic interaction ability and collect successful skill-free trajectories. It then performs self-skill mining, where the current policy summarizes compact skills from its own successful plain rollouts and validates them through paired skill-augmented and skill-free rollouts. Finally, SIRI distills only beneficial skill-guided action tokens into the plain policy using trajectory-level utility and action-level advantage. At inference, the agent runs with the original prompt only. On ALFWorld and WebShop with Qwen2.5-7B-Instruct, SIRI improves GiGPO from 0.908 to 0.930 on ALFWorld and from 0.728 to 0.813 on WebShop, outperforming prompt-based, RL-based, and memory-augmented baselines. Further analysis shows that our self-mining strategy can achieve performance comparable to distillation with closed-source large model. Our code is available at https://github.com/kirito618/SIRI.

cs.AI↗

arXiv2026

Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization

Despite rapid progress in autoregressive video diffusion, an emerging system algorithm bottleneck limits both deployability and generation capability: KV cache memory. In autoregressive video generation models, the KV cache grows with generation history and quickly dominates GPU memory, often exceeding 30 GB, preventing deployment on widely available hardware. More critically, constrained KV cache budgets restrict the effective working memory, directly degrading long horizon consistency in identity, layout, and motion. To address this challenge, we present Quant VideoGen (QVG), a training free KV cache quantization framework for autoregressive video diffusion models. QVG leverages video spatiotemporal redundancy through Semantic Aware Smoothing, producing low magnitude, quantization friendly residuals. It further introduces Progressive Residual Quantization, a coarse to fine multi stage scheme that reduces quantization error while enabling a smooth quality memory trade off. Across LongCat Video, HY WorldPlay, and Self Forcing benchmarks, QVG establishes a new Pareto frontier between quality and memory efficiency, reducing KV cache memory by up to 7.0 times with less than 4% end to end latency overhead while consistently outperforming existing baselines in generation quality. Code is available at: https://github.com/svg-project/Quant-VideoGen

cs.LG↗

arXiv2026

OpenACM: An Open-Source SRAM-Based Approximate CiM Compiler

The rise of data-intensive AI workloads has exacerbated the ``memory wall'' bottleneck. Digital Compute-in-Memory (DCiM) using SRAM offers a scalable solution, but its vast design space makes manual design impractical, creating a need for automated compilers. A key opportunity lies in approximate computing, which leverages the error tolerance of AI applications for significant energy savings. However, existing DCiM compilers focus on exact arithmetic, failing to exploit this optimization. This paper introduces OpenACM, the first open-source, accuracy-aware compiler for SRAM-based approximate DCiM architectures. OpenACM bridges the gap between application error tolerance and hardware automation. Its key contribution is an integrated library of accuracy-configurable multipliers (exact, tunable approximate, and logarithmic), enabling designers to make fine-grained accuracy-energy trade-offs. The compiler automates the generation of the DCiM architecture, integrating a transistor-level customizable SRAM macro with variation-aware characterization into a complete, open-source physical design flow based on OpenROAD and the FreePDK45 library. This ensures full reproducibility and accessibility, removing dependencies on proprietary tools. Experimental results on representative convolutional neural networks (CNNs) demonstrate that OpenACM achieves energy savings of up to 64\% with negligible loss in application accuracy. The framework is available on \href{https://github.com/ShenShan123/OpenACM}{OpenACM:URL}

cs.AR↗

arXiv2025

Nebula: Enable City-Scale 3D Gaussian Splatting in Virtual Reality via Collaborative Rendering and Accelerated Stereo Rasterization

3D Gaussian splatting (3DGS) has drawn significant attention in the architectural community recently. However, current architectural designs often overlook the 3DGS scalability, making them fragile for extremely large-scale 3DGS. Meanwhile, the VR bandwidth requirement makes it impossible to deliver high-fidelity and smooth VR content from the cloud. We present Nebula, a coherent acceleration framework for large-scale 3DGS collaborative rendering. Instead of streaming videos, Nebula streams intermediate results after the LoD search, reducing 1925% data communication between the cloud and the client. To further enhance the motion-to-photon experience, we introduce a temporal-aware LoD search in the cloud that tames the irregular memory access and reduces redundant data access by exploiting temporal coherence across frames. On the client side, we propose a novel stereo rasterization that enables two eyes to share most computations during the stereo rendering with bit-accurate quality. With minimal hardware augmentations, Nebula achieves 2.7$\times$ motion-to-photon speedup and reduces 1925% bandwidth over lossy video streaming.

cs.AR↗

arXiv2025

BalanceGS: Algorithm-System Co-design for Efficient 3D Gaussian Splatting Training on GPU

3D Gaussian Splatting (3DGS) has emerged as a promising 3D reconstruction technique. The traditional 3DGS training pipeline follows three sequential steps: Gaussian densification, Gaussian projection, and color splatting. Despite its promising reconstruction quality, this conventional approach suffers from three critical inefficiencies: (1) Skewed density allocation during Gaussian densification, (2) Imbalanced computation workload during Gaussian projection and (3) Fragmented memory access during color splatting. To tackle the above challenges, we introduce BalanceGS, the algorithm-system co-design for efficient training in 3DGS. (1) At the algorithm level, we propose heuristic workload-sensitive Gaussian density control to automatically balance point distributions - removing 80% redundant Gaussians in dense regions while filling gaps in sparse areas. (2) At the system level, we propose Similarity-based Gaussian sampling and merging, which replaces the static one-to-one thread-pixel mapping with adaptive workload distribution - threads now dynamically process variable numbers of Gaussians based on local cluster density. (3) At the mapping level, we propose reordering-based memory access mapping strategy that restructures RGB storage and enables batch loading in shared memory. Extensive experiments demonstrate that compared with 3DGS, our approach achieves a 1.44$\times$ training speedup on a NVIDIA A100 GPU with negligible quality degradation.

cs.CV↗

arXiv2025

OpenYield: An Open-Source SRAM Yield Analysis and Optimization Benchmark Suite

Static Random-Access Memory (SRAM) yield analysis is essential for semiconductor innovation, yet research progress faces a critical challenge: the large gap between simplified academic models and the complexities observed in practice. The lack of open, higher-fidelity benchmarks has hindered reproducibility and transferability, as promising academic techniques often fail to carry over to more realistic settings. We present OpenYield, an open-source ecosystem that aims to narrow this gap through three contributions: (i) An SRAM circuit generator that explicitly incorporates second-order effects (interconnect/line parasitics, inter-cell leakage coupling, and peripheral-circuit variations) that are commonly omitted in academic studies. (ii) A standardized evaluation platform with a simple interface and baseline yield-analysis implementations to enable fair comparisons and reproducible research on these higher-fidelity circuits. (iii) An optimization platform for transistor-level sizing under these models, supporting reproducible studies of robustness/efficiency trade-offs. OpenYield aims to foster more reproducible and transferable progress in SRAM-yield research. The framework is publicly available at https://github.com/ShenShan123/OpenYield

cs.AR↗

arXiv2025

SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity

Rendering is critical in fields like 3D modeling, AR/VR, and autonomous driving, where high-quality, real-time output is essential. Point-based neural rendering (PBNR) offers a photorealistic and efficient alternative to conventional methods, yet it is still challenging to achieve real-time rendering on mobile platforms. We pinpoint two major bottlenecks in PBNR pipelines: LoD search and splatting. LoD search suffers from workload imbalance and irregular memory access, making it inefficient on off-the-shelf GPUs. Meanwhile, splatting introduces severe warp divergence across GPU threads due to its inherent sparsity. To tackle these challenges, we propose SLTarch, an algorithm-architecture co-designed framework. At its core, SLTarch introduces SLTree, a dedicated subtree-based data structure, and LTcore, a specialized hardware architecture tailored for efficient LoD search. Additionally, we co-design a divergence-free splatting algorithm with our simple yet principled hardware augmentation, SPcore, to existing PBNR accelerators. Compared to a mobile GPU, SLTarch achieves 3.9$\times$ speedup and 98\% energy savings with negligible architecture overhead. Compared to existing accelerator designs, SLTarch achieves 1.8$\times$ speedup with 54\% energy savings.

cs.AR↗

arXiv2025

Radial Attention: $O(n\log n)$ Sparse Attention with Energy Decay for Long Video Generation

Recent advances in diffusion models have enabled high-quality video generation, but the additional temporal dimension significantly increases computational costs, making training and inference on long videos prohibitively expensive. In this paper, we identify a phenomenon we term Spatiotemporal Energy Decay in video diffusion models: post-softmax attention scores diminish as spatial and temporal distance between tokens increase, akin to the physical decay of signal or waves over space and time in nature. Motivated by this, we propose Radial Attention, a scalable sparse attention mechanism with $\mathcal{O}(n \log n)$ complexity that translates energy decay into exponentially decaying compute density, which is significantly more efficient than standard $\mathcal{O}(n^2)$ dense attention and more expressive than linear attention. Specifically, Radial Attention employs a simple, static attention mask where each token attends to spatially nearby tokens, with the attention window size shrinking with temporal distance. Moreover, it allows pre-trained video diffusion models to extend their generation length with efficient LoRA-based fine-tuning. Extensive experiments show that Radial Attention maintains video quality across Wan2.1-14B, HunyuanVideo, and Mochi 1, achieving up to a 1.9$\times$ speedup over the original dense attention. With minimal tuning, it enables video generation up to 4$\times$ longer while reducing training costs by up to 4.4$\times$ compared to direct fine-tuning and accelerating inference by up to 3.7$\times$ compared to dense attention inference. Code is released at \href{https://github.com/mit-han-lab/radial-attention}{https://github.com/mit-han-lab/radial-attention}.

cs.CV↗

arXiv2025

KETA: Kinematic-Phrases-Enhanced Text-to-Motion Generation via Fine-grained Alignment

Motion synthesis plays a vital role in various fields of artificial intelligence. Among the various conditions of motion generation, text can describe motion details elaborately and is easy to acquire, making text-to-motion(T2M) generation important. State-of-the-art T2M techniques mainly leverage diffusion models to generate motions with text prompts as guidance, tackling the many-to-many nature of T2M tasks. However, existing T2M approaches face challenges, given the gap between the natural language domain and the physical domain, making it difficult to generate motions fully consistent with the texts. We leverage kinematic phrases(KP), an intermediate representation that bridges these two modalities, to solve this. Our proposed method, KETA, decomposes the given text into several decomposed texts via a language model. It trains an aligner to align decomposed texts with the KP segments extracted from the generated motions. Thus, it's possible to restrict the behaviors for diffusion-based T2M models. During the training stage, we deploy the text-KP alignment loss as an auxiliary goal to supervise the models. During the inference stage, we refine our generated motions for multiple rounds in our decoder structure, where we compute the text-KP distance as the guidance signal in each new round. Experiments demonstrate that KETA achieves up to 1.19x, 2.34x better R precision and FID value on both backbones of the base model, motion diffusion model. Compared to a wide range of T2M generation models. KETA achieves either the best or the second-best performance.

cs.CV↗