1 citations · 1 across the 19 of their papers we have counts for
27 papers
SetCommit: Coordinated Token Commitments for Few-Step Diffusion Language Model Decoding
Yuannuo Feng, Yubing Ye, Wenshuai Yao +4
Diffusion language models generate multiple token positions in parallel, but few-step decoding makes parallel commitment difficult: tokens that appear reliable individually may be…
Share the Symbol, Not the Vector: Training-Free Fact Communication Across Language Models
Xin Li, Mengbing Liu, Hao Jiang +5
Multi-agent systems allow language models to solve tasks collaboratively by sharing complementary information. These systems typically communicate through text, while recent approa…
ScaleLUT: A Fully-Parallel Configurable LUT-Based Accelerator for Real-Time Multi-Scale Super-Resolution
Boyu Li, Chenchen Ding, Zhilin Ai +7
Real-time super-resolution (SR) remains challenging for edge devices because deep-learning-based methods require substantial multiply-accumulate (MAC) operations, resources, and po…
NOVA-CIM: Noise- and Correlation-Tolerant Stochastic Interfaces for Analog Compute-in-Memory
Jiachen Ren, Wenshuai Yao, Haobo Liu +6
Analog compute-in-memory (CIM) enables energy-efficient model acceleration, but its reliance on ADC-based readout, which directly quantizes noisy column currents, makes inference a…
ASSERT: Adaptive Stochastic Sampling for Robust Diffusion Models on Analog Compute-in-Memory Hardware
Yuannuo Feng, Yizhe Chen, Wenshuai Yao +4
Diffusion models achieve strong image generation quality but incur high iterative denoising costs. Analog compute-in-memory (CIM) can accelerate matrix-vector multiplications, yet…
Approximate Speculative Decoding
Yuannuo Feng, Zegang Peng, Yuxin Xie +5
Speculative decoding accelerates autoregressive generation by verifying a draft block with a target model in parallel. Under standard greedy verification, decoding stops at the fir…
When Guidance Goes Off-Scale: Recalibrating Diffusion Transformers under Analog Compute-in-Memory Nonidealities
Wenshuai Yao, Wenyong Zhou
Diffusion Transformers (DiTs) incur high memory traffic and energy costs because sampling repeatedly evaluates large denoisers dominated by linear operations. Analog compute-in-mem…
Selective KV Cache Protection for Noise-Resilient LLM Inference on Analog Compute-In-Memory Systems
Yuannuo Feng, Wenyong Zhou, Yuang Ma +5
Analog compute-in-memory (CIM) arrays have emerged as a promising substrate for energy-efficient LLM inference, particularly for weight-stationary computations in linear layers. Ho…
Hybrid-LUT: Channel-Aware Hybrid Lookup Table and Filtering for Efficient Image Denoising
Zhilin Ai, Boyu Li, Sidi Yang +5
Lookup table (LUT)-based image denoising methods have attracted increasing attention due to their high efficiency and hardware-friendly properties. However, existing RGB-LUT approa…
NANQ: Noise-Floor-Aware Mixed-Precision Non-Uniform Quantization for Analog Compute-in-Memory
Yizhe Chen, Wenshuai Yao, Saiya Wang +6
Analog compute-in-memory (CIM) enables energy-efficient neural network inference, but device variation and read noise can severely degrade low-bit quantized models. Existing CIM-or…
Recall Before You Rank: Similarity-Guided Top- Reuse for Efficient Long-Context Attention
Wenshuai Yao, Wenyong Zhou, Hanyong Shao +5
Top- sparse attention reduces the cost of Softmax and value aggregation by attending to only a small subset of key--value (KV) entries. However, identifying this subset still re…
PatchINR: Patch-Based Implicit Neural Representations for Efficient and Scalable Inference
Jiachen Ren, Wenyong Zhou, Taiqiang Wu +4
Implicit Neural Representation (INR) provides an effective approach for continuous signal modeling, but classical per-pixel inference results in quadratic growth in inference count…
ROMER: Expert Replacement and Router Calibration for Robust MoE LLMs on Analog Compute-in-Memory Systems
Wenyong Zhou, Yuannuo Feng, Yizhe Chen +6
Large language models (LLMs) with mixture-of-experts (MoE) architectures achieve remarkable scalability by sparsely activating a subset of experts per token, yet their frequent exp…
Can We Trust LLMs on Memristors? Diving into Reasoning Ability under Non-Ideality
Taiqiang Wu, Yuxin Cheng, Chenchen Ding +5
Memristor-based analog compute-in-memory (CIM) architectures provide a promising substrate for the efficient deployment of Large Language Models (LLMs), owing to superior energy ef…
AnchorTP: Resilient LLM Inference with State-Preserving Elastic Tensor Parallelism
Wendong Xu, Chujie Chen, He Xiao +8
Large Language Model (LLM) inference services demand exceptionally high availability and low latency, yet multi-GPU Tensor Parallelism (TP) makes them vulnerable to single-GPU fail…
Re-Activating Frozen Primitives for 3D Gaussian Splatting
Yuxin Cheng, Binxiao Huang, Wenyong Zhou +4
3D Gaussian Splatting (3D-GS) achieves real-time photorealistic novel view synthesis, yet struggles with complex scenes due to over-reconstruction artifacts, manifesting as local b…
Perspective-aware 3D Gaussian Inpainting with Multi-view Consistency
Yuxin Cheng, Binxiao Huang, Taiqiang Wu +5
3D Gaussian inpainting, a critical technique for numerous applications in virtual reality and multimedia, has made significant progress with pretrained diffusion models. However, e…
Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators
Wenyong Zhou, Zhengwu Liu, Yuan Ren +1
Compute-in-memory (CIM) accelerators have emerged as a promising way for enhancing the energy efficiency of convolutional neural networks (CNNs). Deploying CNNs on CIM platforms ge…
QuadINR: Hardware-Efficient Implicit Neural Representations Through Quadratic Activation
Wenyong Zhou, Boyu Li, Jiachen Ren +4
Implicit Neural Representations (INRs) encode discrete signals continuously while addressing spectral bias through activation functions (AFs). Previous approaches mitigate this bia…
Enhancing Robustness of Implicit Neural Representations Against Weight Perturbations
Wenyong Zhou, Yuxin Cheng, Zhengwu Liu +3
Implicit Neural Representations (INRs) encode discrete signals in a continuous manner using neural networks, demonstrating significant value across various multimedia applications.…
Distribution-Aware Hadamard Quantization for Hardware-Efficient Implicit Neural Representations
Wenyong Zhou, Jiachen Ren, Taiqiang Wu +3
Implicit Neural Representations (INRs) encode discrete signals using Multi-Layer Perceptrons (MLPs) with complex activation functions. While INRs achieve superior performance, they…
MINR: Efficient Implicit Neural Representations for Multi-Image Encoding
Wenyong Zhou, Taiqiang Wu, Zhengwu Liu +3
Implicit Neural Representations (INRs) aim to parameterize discrete signals through implicit continuous functions. However, formulating each image with a separate neural network~(t…
A Time- and Energy-Efficient CNN with Dense Connections on Memristor-Based Chips
Wenyong Zhou, Yuan Ren, Jiajun Zhou +2
Designing lightweight convolutional neural network (CNN) models is an active research area in edge AI. Compute-in-memory (CIM) provides a new computing paradigm to alleviate time a…
Extending Straight-Through Estimation for Robust Neural Networks on Analog CIM Hardware
Yuannuo Feng, Wenyong Zhou, Yuexi Lyu +4
Analog Compute-In-Memory (CIM) architectures promise significant energy efficiency gains for neural network inference, but suffer from complex hardware-induced noise that poses maj…