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.
no code implementations • 19 Mar 2024 • Jingyu Lin, Jiaqi Gu, Bojian Wu, Lubin Fan, Renjie Chen, Ligang Liu, Jieping Ye
We introduce a novel neural volumetric pose feature, termed PoseMap, designed to enhance camera localization by encapsulating the information between images and the associated camera poses.
no code implementations • 5 Mar 2024 • Haotian Lu, Sanmitra Banerjee, Jiaqi Gu
While off-chip noise-aware training and on-chip training have been proposed to enhance the variation tolerance of optical neural accelerators with moderate, static noises, we observe a notable performance degradation over time due to temporally drifting variations, which requires a real-time, in-situ calibration mechanism.
no code implementations • 12 Feb 2024 • Meng Zhang, Dennis Yin, Nicholas Gangi, Amir Begović, Alexander Chen, Zhaoran Rena Huang, Jiaqi Gu
Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency of optics, especially for real-time, low-energy deep neural network (DNN) inference tasks on resource-restricted edge platforms.
1 code implementation • 10 Jan 2024 • Tianlong Chen, Zhenyu Zhang, Hanrui Wang, Jiaqi Gu, Zirui Li, David Z. Pan, Frederic T. Chong, Song Han, Zhangyang Wang
To address these two pain points, we propose QuantumSEA, an in-time sparse exploration for noise-adaptive quantum circuits, aiming to achieve two key objectives: (1) implicit circuits capacity during training - by dynamically exploring the circuit's sparse connectivity and sticking a fixed small number of quantum gates throughout the training which satisfies the coherence time and enjoy light noises, enabling feasible executions on real quantum devices; (2) noise robustness - by jointly optimizing the topology and parameters of quantum circuits under real device noise models.
no code implementations • 27 Nov 2023 • Hanrui Wang, Pengyu Liu, Kevin Shao, Dantong Li, Jiaqi Gu, David Z. Pan, Yongshan Ding, Song Han
Quantum Error Correction (QEC) mitigates this by employing redundancy, distributing quantum information across multiple data qubits and utilizing syndrome qubits to monitor their states for errors.
no code implementations • 27 Nov 2023 • Hanrui Wang, Yilian Liu, Pengyu Liu, Jiaqi Gu, Zirui Li, Zhiding Liang, Jinglei Cheng, Yongshan Ding, Xuehai Qian, Yiyu Shi, David Z. Pan, Frederic T. Chong, Song Han
Arbitrary state preparation algorithms can be broadly categorized into arithmetic decomposition (AD) and variational quantum state preparation (VQSP).
no code implementations • 27 Nov 2023 • Hanrui Wang, Pengyu Liu, Yilian Liu, Jiaqi Gu, Jonathan Baker, Frederic T. Chong, Song Han
By counting the occurrences of edges and edge pairs in decoded matchings, we can statistically estimate the up-to-date probabilities of each edge and the correlations between them.
1 code implementation • 31 May 2023 • Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang, Ray T. Chen, David Z. Pan
The programmable MOMMI leverages the intrinsic light propagation principle, providing a single-device programmable matrix unit beyond the conventional computing paradigm of one multiply-accumulate (MAC) operation per device.
no code implementations • 31 May 2023 • Chenghao Feng, Jiaqi Gu, Hanqing Zhu, Rongxing Tang, Shupeng Ning, May Hlaing, Jason Midkiff, Sourabh Jain, David Z. Pan, Ray T. Chen
The optical neural network (ONN) is a promising hardware platform for next-generation neuromorphic computing due to its high parallelism, low latency, and low energy consumption.
1 code implementation • NeurIPS 2023 • Zixuan Jiang, Jiaqi Gu, Hanqing Zhu, David Z. Pan
Experiments demonstrate that we can reduce the training and inference time of Pre-LN Transformers by 1% - 10%.
no code implementations • 30 Nov 2022 • Jiaqi Gu, Ben Keller, Jean Kossaifi, Anima Anandkumar, Brucek Khailany, David Z. Pan
Transformers have attained superior performance in natural language processing and computer vision.
1 code implementation • 30 Oct 2022 • Hanrui Wang, Pengyu Liu, Jinglei Cheng, Zhiding Liang, Jiaqi Gu, Zirui Li, Yongshan Ding, Weiwen Jiang, Yiyu Shi, Xuehai Qian, David Z. Pan, Frederic T. Chong, Song Han
Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency.
1 code implementation • 19 Sep 2022 • Jiaqi Gu, Zhengqi Gao, Chenghao Feng, Hanqing Zhu, Ray T. Chen, Duane S. Boning, David Z. Pan
In this work, for the first time, a physics-agnostic neural operator-based framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain Maxwell PDEs for ultra-fast parametric photonic device simulation.
1 code implementation • 30 Jul 2022 • Zixuan Jiang, Jiaqi Gu, Mingjie Liu, David Z. Pan
In this work, we delve into the gradient matching method from a comprehensive perspective and answer the critical questions of what, how, and where to match.
no code implementations • 13 Jul 2022 • Wei Shi, Hanrui Wang, Jiaqi Gu, Mingjie Liu, David Pan, Song Han, Nan Sun
To address the challenge, we present RobustAnalog, a robust circuit design framework that involves the variation information in the optimization process.
1 code implementation • CVPR 2022 • Jiaqi Gu, Bojian Wu, Lubin Fan, Jianqiang Huang, Shen Cao, Zhiyu Xiang, Xian-Sheng Hua
Monocular 3D object detection is an essential task in autonomous driving.
no code implementations • 13 Mar 2022 • Jiaqi Gu, Zhiyu Xiang, Pan Zhao, Tingming Bai, Lingxuan Wang, Xijun Zhao, Zhiyuan Zhang
In recent years 3D object detection from LiDAR point clouds has made great progress thanks to the development of deep learning technologies.
1 code implementation • 26 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.
no code implementations • 15 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.
1 code implementation • 11 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 hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption.
1 code implementation • CVPR 2022 • 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.
Ranked #25 on Semantic Segmentation on Cityscapes val
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.
2 code implementations • 21 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.
no code implementations • 29 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.
no code implementations • 29 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.
no code implementations • 28 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.
1 code implementation • 25 Aug 2021 • Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen, David Z. Pan
Deep neural networks (DNN) have shown superior performance in a variety of tasks.
2 code implementations • 22 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.
no code implementations • 1 Apr 2021 • Zixuan Jiang, Jiaqi Gu, Mingjie Liu, Keren Zhu, David Z. Pan
Machine learning frameworks adopt iterative optimizers to train neural networks.
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.
1 code implementation • 21 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.
1 code implementation • 4 Dec 2020 • Shubham Rai, Walter Lau Neto, Yukio Miyasaka, Xinpei Zhang, Mingfei Yu, Qingyang Yi Masahiro Fujita, Guilherme B. Manske, Matheus F. Pontes, Leomar S. da Rosa Junior, Marilton S. de Aguiar, Paulo F. Butzen, Po-Chun Chien, Yu-Shan Huang, Hoa-Ren Wang, Jie-Hong R. Jiang, Jiaqi Gu, Zheng Zhao, Zixuan Jiang, David Z. Pan, Brunno A. de Abreu, Isac de Souza Campos, Augusto Berndt, Cristina Meinhardt, Jonata T. Carvalho, Mateus Grellert, Sergio Bampi, Aditya Lohana, Akash Kumar, Wei Zeng, Azadeh Davoodi, Rasit O. Topaloglu, Yuan Zhou, Jordan Dotzel, Yichi Zhang, Hanyu Wang, Zhiru Zhang, Valerio Tenace, Pierre-Emmanuel Gaillardon, Alan Mishchenko, Satrajit Chatterjee
If the function is incompletely-specified, the implementation has to be true only on the care set.