Search Results for author: Yinhe Han

Found 8 papers, 4 papers with code

Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework

no code implementations17 Mar 2024 Kaiyan Chang, Kun Wang, Nan Yang, Ying Wang, Dantong Jin, Wenlong Zhu, Zhirong Chen, Cangyuan Li, Hao Yan, Yunhao Zhou, Zhuoliang Zhao, Yuan Cheng, Yudong Pan, Yiqi Liu, Mengdi Wang, Shengwen Liang, Yinhe Han, Huawei Li, Xiaowei Li

Our 13B model (ChipGPT-FT) has a pass rate improvement compared with GPT-3. 5 in Verilog generation and outperforms in EDA script (i. e., SiliconCompiler) generation with only 200 EDA script data.

Data Augmentation

ChipGPT: How far are we from natural language hardware design

no code implementations23 May 2023 Kaiyan Chang, Ying Wang, Haimeng Ren, Mengdi Wang, Shengwen Liang, Yinhe Han, Huawei Li, Xiaowei Li

As large language models (LLMs) like ChatGPT exhibited unprecedented machine intelligence, it also shows great performance in assisting hardware engineers to realize higher-efficiency logic design via natural language interaction.

Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization

1 code implementation30 Mar 2023 Hanqi Jiang, Cheng Zeng, Runnan Chen, Shuai Liang, Yinhe Han, Yichao Gao, Conglin Wang

To address this problem, we propose a neural implicit surface learning method called Depth-NeuS based on depth information optimization for multi-view reconstruction.

Object Reconstruction Surface Reconstruction

AGNAS: Attention-Guided Micro- and Macro-Architecture Search

1 code implementation International Conference on Machine Learning 2022 Zihao Sun, Yu Hu, Shun Lu, Longxing Yang, Jilin Mei, Yinhe Han, Xiaowei Li

We utilize the attention weights to represent the importance of the relevant operations for the micro search or the importance of the relevant blocks for the macro search.

Neural Architecture Search

Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals

no code implementations30 Jan 2022 Weidong Cao, Yilong Zhao, Adith Boloor, Yinhe Han, Xuan Zhang, Li Jiang

This paper presents a new PIM architecture to efficiently accelerate deep learning tasks by minimizing the required A/D conversions with analog accumulation and neural approximated peripheral circuits.

Quantization

Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction

1 code implementation CVPR 2020 Beibei Jin, Yu Hu, Qiankun Tang, Jingyu Niu, Zhiping Shi, Yinhe Han, Xiaowei Li

Inspired by the frequency band decomposition characteristic of Human Vision System (HVS), we propose a video prediction network based on multi-level wavelet analysis to deal with spatial and temporal information in a unified manner.

 Ranked #1 on Video Prediction on KTH (PSNR metric)

Video Generation Video Prediction

Communication Lower Bound in Convolution Accelerators

no code implementations8 Nov 2019 Xiaoming Chen, Yinhe Han, Yu Wang

Evaluations based on the 65nm technology demonstrate that the proposed architecture nearly reaches the theoretical minimum communication in a three-level memory hierarchy and it is computation dominant.

Distributed, Parallel, and Cluster Computing Hardware Architecture

See and Think: Disentangling Semantic Scene Completion

1 code implementation NeurIPS 2018 Shice Liu, Yu Hu, Yiming Zeng, Qiankun Tang, Beibei Jin, Yinhe Han, Xiaowei Li

Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings.

2D Semantic Segmentation 3D Semantic Scene Completion +2

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