Search Results for author: Jiaqi Gu

Found 18 papers, 10 papers with code

An Efficient Training Framework for Reversible Neural Architectures

no code implementations ECCV 2020 Zixuan Jiang, Keren Zhu, Mingjie Liu, Jiaqi Gu, David Z. Pan

In this work, we formulate the decision problem for reversible operators with training time as the objective function and memory usage as the constraint.

CVFNet: Real-time 3D Object Detection by Learning Cross View Features

no code implementations13 Mar 2022 Jiaqi Gu, Zhiyu Xiang, Pan Zhao, Tingming Bai, Lingxuan Wang, Zhiyuan Zhang

In recent years 3D object detection from LiDAR point clouds has made great progress thanks to the development of deep learning technologies.

3D Object Detection

QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning

2 code implementations26 Feb 2022 Hanrui Wang, Zirui Li, Jiaqi Gu, Yongshan Ding, David Z. Pan, Song Han

Nevertheless, we find that due to the significant quantum errors (noises) on real machines, gradients obtained from naive parameter shift have low fidelity and thus degrading the training accuracy.

Image Classification

ELight: Enabling Efficient Photonic In-Memory Neurocomputing with Life Enhancement

no code implementations15 Dec 2021 Hanqing Zhu, Jiaqi Gu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen, David Z. Pan

With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with near-zero static power consumption, time-of-light latency, and compact footprint.

Silicon photonic subspace neural chip for hardware-efficient deep learning

1 code implementation11 Nov 2021 Chenghao Feng, Jiaqi Gu, Hanqing Zhu, Zhoufeng Ying, Zheng Zhao, David Z. Pan, Ray T. Chen

The optical neural network (ONN) is a promising candidate for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption.

Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation

no code implementations1 Nov 2021 Jiaqi Gu, Hyoukjun Kwon, Dilin Wang, Wei Ye, Meng Li, Yu-Hsin Chen, Liangzhen Lai, Vikas Chandra, David Z. Pan

Therefore, we propose HRViT, which enhances ViTs to learn semantically-rich and spatially-precise multi-scale representations by integrating high-resolution multi-branch architectures with ViTs.

Image Classification Representation Learning +1

L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization

1 code implementation NeurIPS 2021 Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang, Ray T. Chen, David Z. Pan

In this work, we propose a closed-loop ONN on-chip learning framework L2ight to enable scalable ONN mapping and efficient in-situ learning.

QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization

2 code implementations21 Oct 2021 Hanrui Wang, Jiaqi Gu, Yongshan Ding, Zirui Li, Frederic T. Chong, David Z. Pan, Song Han

Furthermore, to improve the robustness against noise, we propose noise injection to the training process by inserting quantum error gates to PQC according to realistic noise models of quantum hardware.

Denoising Quantization

Towards Efficient On-Chip Training of Quantum Neural Networks

no code implementations29 Sep 2021 Hanrui Wang, Zirui Li, Jiaqi Gu, Yongshan Ding, David Z. Pan, Song Han

The results demonstrate that our on-chip training achieves over 90% and 60% accuracy for 2-class and 4-class image classification tasks.

Image Classification

RoDesigner: Variation-Aware Optimization for Robust Analog Design with Multi-Task RL

no code implementations29 Sep 2021 Wei Shi, Hanrui Wang, Jiaqi Gu, Mingjie Liu, David Z. Pan, Song Han, Nan Sun

Specifically, circuit optimizations under different variations are considered as a set of tasks.

DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network

no code implementations28 Aug 2021 Jiaqi Gu, Zhiyu Xiang, Yuwen Ye, Lingxuan Wang

Depth Completion can produce a dense depth map from a sparse input and provide a more complete 3D description of the environment.

3D Object Detection Depth Completion +1

QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

2 code implementations22 Jul 2021 Hanrui Wang, Yongshan Ding, Jiaqi Gu, Zirui Li, Yujun Lin, David Z. Pan, Frederic T. Chong, Song Han

Extensively evaluated with 12 QML and VQE benchmarks on 14 quantum computers, QuantumNAS significantly outperforms baselines.

Optimizer Fusion: Efficient Training with Better Locality and Parallelism

no code implementations1 Apr 2021 Zixuan Jiang, Jiaqi Gu, Mingjie Liu, Keren Zhu, David Z. Pan

Machine learning frameworks adopt iterative optimizers to train neural networks.

SqueezeLight: Towards Scalable Optical Neural Networks with Multi-Operand Ring Resonators

1 code implementation IEEE Design, Automation & Test in Europe Conference & Exhibition (DATE) 2021 Jiaqi Gu, Chenghao Feng, Zheng Zhao, Zhoufeng Ying, Mingjie Liu, Ray T. Chen, David Z. Pan

Optical neural networks (ONNs) have demonstrated promising potentials for next-generation artificial intelligence acceleration with ultra-low latency, high bandwidth, and low energy consumption.

Efficient On-Chip Learning for Optical Neural Networks Through Power-Aware Sparse Zeroth-Order Optimization

1 code implementation21 Dec 2020 Jiaqi Gu, Chenghao Feng, Zheng Zhao, Zhoufeng Ying, Ray T. Chen, David Z. Pan

Optical neural networks (ONNs) have demonstrated record-breaking potential in high-performance neuromorphic computing due to their ultra-high execution speed and low energy consumption.

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