46 citations · 47 across the 16 of their papers we have counts for
16 papers
Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
Yuxuan Yin, Chen He, Todd Jacobs +4
Latent defect screening is challenged by extremely low failure rates, high-dimensional test data, and absence of labeled anomalies. We propose the first unsupervised anomaly detect…
Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation
Boxun Xu, Yuming Du, Zichang Liu +7
We introduce Sparse Forcing, a training-and-inference paradigm for autoregressive video diffusion models that improves long-horizon generation quality while reducing decoding laten…
VEGAS: Mitigating Hallucinations in Large Vision-Language Models via Vision-Encoder Attention Guided Adaptive Steering
Zihu Wang, Boxun Xu, Yuxuan Xia +1
Large vision-language models (LVLMs) exhibit impressive ability to jointly reason over visual and textual inputs. However, they often produce outputs that are linguistically fluent…
LASER: Language Model Regression for Semi-Structured Workflow Resource and Runtime Estimation
Yuxuan Yin, Shengke Zhou, Yunjie Zhang +3
Accurate prediction of resource consumption and runtime for cloud workflow jobs is critical for scheduling efficiency, yet remains challenging due to the semi-structured nature of…
AMS-KV: Adaptive KV Caching in Multi-Scale Visual Autoregressive Transformers
Boxun Xu, Yu Wang, Zihu Wang +1
Visual autoregressive modeling (VAR) via next-scale prediction has emerged as a scalable image generation paradigm. While Key and Value (KV) caching in large language models (LLMs)…
Transfer Learning for Minimum Operating Voltage Prediction in Advanced Technology Nodes: Leveraging Legacy Data and Silicon Odometer Sensing
Yuxuan Yin, Rebecca Chen, Boxun Xu +2
Accurate prediction of chip performance is critical for ensuring energy efficiency and reliability in semiconductor manufacturing. However, developing minimum operating voltage ($V…
SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks
Boxun Xu, Richard Boone, Peng Li
Spiking Neural Networks (SNNs) are promising biologically plausible models of computation which utilize a spiking binary activation function similar to that of biological neurons.…
Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained Pruning
Boxun Xu, Yuxuan Yin, Vikram Iyer +1
We present Bishop, the first dedicated hardware accelerator architecture and HW/SW co-design framework for spiking transformers that optimally represents, manages, and processes sp…
Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives
Zihu Wang, Boxun Xu, Hejia Geng +1
Graph contrastive learning (GCL) has demonstrated great promise for learning generalizable graph representations from unlabeled data. However, conventional GCL approaches face two…
Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers
Boxun Xu, Junyoung Hwang, Pruek Vanna-iampikul +3
Spiking Neural Networks(SNNs) provide a brain-inspired and event-driven mechanism that is believed to be critical to unlock energy-efficient deep learning. The mixture-of-experts a…
Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search
Boxun Xu, Yufei Song, Peng Li
Spiking Neural Networks (SNNs) are amenable to deployment on edge devices and neuromorphic hardware due to their lower dissipation. Recently, SNN-based transformers have garnered s…
Spiking Transformer Hardware Accelerators in 3D Integration
Boxun Xu, Junyoung Hwang, Pruek Vanna-iampikul +2
Spiking neural networks (SNNs) are powerful models of spatiotemporal computation and are well suited for deployment on resource-constrained edge devices and neuromorphic hardware d…
DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention
Boxun Xu, Hejia Geng, Yuxuan Yin +1
Vision Transformers (ViT) are current high-performance models of choice for various vision applications. Recent developments have given rise to biologically inspired spiking transf…
ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models
Yuxuan Yin, Yu Wang, Boxun Xu +1
Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine l…
DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention
Boxun Xu, Hejia Geng, Yuxuan Yin +1
Among the array of neural network architectures, the Vision Transformer (ViT) stands out as a prominent choice, acclaimed for its exceptional expressiveness and consistent high per…
UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities
Hejia Geng, Boxun Xu, Peng Li
Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting. Despite t…