10 citations · 10 across the 9 of their papers we have counts for
10 papers
An Emerging NVM-Based On-Chip Training Architecture with Non-Ideality Mitigation Through Bipolar Weight Distributions
Peng Dang, Youna Huang, Yintao He +1
The rapid advancement of deep learning has presented significant energy efficiency challenges to the conventional von Neumann architecture. In-memory computing (IMC) architectures…
VARA: A Voltage-Aware ReRAM-Based Accelerator for Energy-Efficient Computing
Peng Dang, Yintao He, Huawei Li
ReRAM-based in-memory computing (IMC) architectures are widely regarded as a promising approach to alleviating the computational bottleneck of conventional architectures. Since ReR…
Multi-primitive in-memory computing for Monte Carlo tree search
Tergel Molom-Ochir, Benjamin F. Morris, Yintao He +6
Monte Carlo tree search (MCTS) enables artificial intelligence (AI) decision-making, but requires 55-300 W on conventional processors, limiting edge deployment. In-memory computing…
TriMoE: Augmenting GPU with AMX-Enabled CPU and DIMM-NDP for High-Throughput MoE Inference via Offloading
Yudong Pan, Yintao He, Tianhua Han +7
To deploy large Mixture-of-Experts (MoE) models cost-effectively, offloading-based single-GPU heterogeneous inference is crucial. While GPU-CPU architectures that offload cold expe…
PAM: Processing Across Memory Hierarchy for Efficient KV-centric LLM Serving System
Lian Liu, Shixin Zhao, Yutian Zhou +4
The widespread adoption of Large Language Models (LLMs) has exponentially increased the demand for efficient serving systems. With growing requests and context lengths, key-value (…
DPad: Efficient Diffusion Language Models with Suffix Dropout
Xinhua Chen, Sitao Huang, Cong Guo +5
Diffusion-based Large Language Models (dLLMs) parallelize text generation by framing decoding as a denoising process, but suffer from high computational overhead since they predict…
OASIS: Outlier-Aware LUT-Based GEMM with Dual-Side Quantization for LLM Inference Acceleration
Xueying Wu, Baijun Zhou, Zhihui Gao +4
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of applications, but demand substantial memory and compute resources during inference. Ex…
PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System
Yintao He, Haiyu Mao, Christina Giannoula +6
Large language models (LLMs) are widely used for natural language understanding and text generation. An LLM model relies on a time-consuming step called LLM decoding to generate ou…
Be CIM or Be Memory: A Dual-mode-aware DNN Compiler for CIM Accelerators
Shixin Zhao, Yuming Li, Bing Li +4
Computing-in-memory (CIM) architectures demonstrate superior performance over traditional architectures. To unleash the potential of CIM accelerators, many compilation methods have…
CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory Accelerators
Songyun Qu, Shixin Zhao, Bing Li +4
In recent years, various computing-in-memory (CIM) processors have been presented, showing superior performance over traditional architectures. To unleash the potential of various…