21 citations · 47 across the 17 of their papers we have counts for
17 papers
CODA: Algorithm-Hardware Co-design for Edge Video Diffusion via NMP-Enabled Compute-Cache Operator Disaggregation
Yuanpeng Zhang, YuXuan Wu, Yitong Xiao +6
Deploying Video Diffusion Models (VDMs) on edge devices is appealing for localized and privacy-preserving generation, but their iterative Transformer-based denoising remains too sl…
Isolation-aware Scheduling Framework for DNN-based End-to-End Autonomous Driving System on Tile-based Accelerators
Chenguang Zhang, Yuanpeng Zhang, Chenhao Xue +3
Level-4+ autonomous driving systems (ADS) must run dozens of heterogeneous deep neural networks (DNNs) as end-to-end (E2E) pipelines under a strict latency constraint (<=100 ms), e…
AccelCIM: Systematic Dataflow Exploration for SRAM Compute-in-Memory Accelerator
Chenhao Xue, Yukun Wang, An Guo +11
SRAM-based compute-in-memory (CIM) offers high computational density and energy efficiency for deep neural network (DNN) accelerators, but its limited capacity causes on/off-chip d…
A Full-Stack Performance Evaluation Infrastructure for 3D-DRAM-based LLM Accelerators
Cong Li, Chenhao Xue, Yi Ren +11
Large language models (LLMs) exhibit memory-intensive behavior during decoding, making it a key bottleneck in LLM inference. To accelerate decoding execution, hybrid-bonding-based…
Hardware-Software Co-design for 3D-DRAM-based LLM Serving Accelerator
Cong Li, Yihan Yin, Chenhao Xue +7
Large language models (LLMs) have been widely deployed for online generative services, where numerous LLM instances jointly handle workloads with fluctuating request arrival rates…
Orthrus: Dual-Loop Automated Framework for System-Technology Co-Optimization
Yi Ren, Baokang Peng, Chenhao Xue +6
With the diminishing return from Moore's Law, system-technology co-optimization (STCO) has emerged as a promising approach to sustain the scaling trends in the VLSI industry. By br…
MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs
Chenchen Zhao, Zhengyuan Shi, Xiangyu Wen +19
The emergence of multimodal large language models (MLLMs) presents promising opportunities for automation and enhancement in Electronic Design Automation (EDA). However, comprehens…
AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models
Chenhao Xue, Kezhi Li, Jiaxing Zhang +7
Arithmetic circuits, such as adders and multipliers, are fundamental components of digital systems, directly impacting the performance, power efficiency, and area footprint. Howeve…
ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA
Zhengyuan Shi, Zeju Li, Chengyu Ma +19
We introduce ForgeEDA, an open-source comprehensive circuit dataset across various categories. ForgeEDA includes diverse circuit representations such as Register Transfer Level (RT…
DiffuSE: Cross-Layer Design Space Exploration of DNN Accelerator via Diffusion-Driven Optimization
Yi Ren, Chenhao Xue, Jiaxing Zhang +5
The proliferation of deep learning accelerators calls for efficient and cost-effective hardware design solutions, where parameterized modular hardware generator and electronic desi…
DOMAC: Differentiable Optimization for High-Speed Multipliers and Multiply-Accumulators
Chenhao Xue, Yi Ren, Jinwei Zhou +6
Multipliers and multiply-accumulators (MACs) are fundamental building blocks for compute-intensive applications such as artificial intelligence. With the diminishing returns of Moo…
Theseus: Exploring Efficient Wafer-Scale Chip Design for Large Language Models
Jingchen Zhu, Chenhao Xue, Yiqi Chen +13
The emergence of the large language model~(LLM) poses an exponential growth of demand for computation throughput, memory capacity, and communication bandwidth. Such a demand growth…
The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models
Lei Chen, Yiqi Chen, Zhufei Chu +36
Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies.…
LLM Inference Unveiled: Survey and Roofline Model Insights
Zhihang Yuan, Yuzhang Shang, Yang Zhou +11
The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is v…
Latency-aware Spatial-wise Dynamic Networks
Yizeng Han, Zhihang Yuan, Yifan Pu +4
Spatial-wise dynamic convolution has become a promising approach to improving the inference efficiency of deep networks. By allocating more computation to the most informative pixe…
PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization
Zhihang Yuan, Chenhao Xue, Yiqi Chen +2
Quantization is one of the most effective methods to compress neural networks, which has achieved great success on convolutional neural networks (CNNs). Recently, vision transforme…
PTQ-SL: Exploring the Sub-layerwise Post-training Quantization
Zhihang Yuan, Yiqi Chen, Chenhao Xue +3
Network quantization is a powerful technique to compress convolutional neural networks. The quantization granularity determines how to share the scaling factors in weights, which a…