10 papers
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…
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…
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…
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…
Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models
He Xiao, Qingyao Yang, Dirui Xie +7
Large language models with billions of parameters are often over-provisioned: many layers contribute little unique information yet dominate the memory and energy footprint during i…
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.…