1 citations · 1 across the 17 of their papers we have counts for
24 papers
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…