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research.html
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<!DOCTYPE html>
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<li>The Inaugural <a href="https://cpal.cc/"><b>Conference on Parsimony and Learning (CPAL)</b></a>, Hong Kong, Jan 2024</li>
<li>CVPR 2023 Workshop and Prize Challenge: <a href="http://cvpr2023.ug2challenge.org/">Bridging the Gap between Computational Photography and Visual Recognition (UG2+)</a>, Jun 2023</li>
<li>ICASSP 2023 Special Session on Unsupervised Deep Learning of Image Priors for Inverse Problems, Jun 2023</li>
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<h2>Research Interests</h2>
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<div class="trend-entry d-flex">
Under Construction
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<!-- <div class="trend-entry d-flex">-->
<!-- <div class="trend-contents">-->
<!-- <h4>Our Goals -- Enable Next-Generation Intelligent Computing Systems </h4>-->
<!-- <p>-->
<!-- We seek to build next-generation intelligent computing systems with emerging-->
<!-- semiconductor technologies beyond conventional digital electronics. Our group's-->
<!-- research-->
<!-- will target enabling computing systems with (1) ultra-fast-->
<!-- processing speed-->
<!-- (e.g., low latency, high throughput), (2) superior efficiency (e.g., compute-->
<!-- density,-->
<!-- power/energy efficiency), (3) high robustness against both static (systematic) and-->
<!-- dynamic (random) noises/variations/attacks/aging, and (4) great adaptability to-->
<!-- support-->
<!-- advanced, dynamic intelligent workloads in pratical application scenarios (e.g.,-->
<!-- cloud,-->
<!-- resource-limited edge, distributed systems).-->
<!-- </p>-->
<!-- <p>Towards this ultimate goal, we focus on developing future computing platforms based-->
<!-- on:-->
<!-- <li><b>Heterogeneous Integration</b>: Mixed-signal heterogneous technologies-->
<!-- (analog,-->
<!-- digital, electronics, photonics, and beyond) will be fused into the systems with-->
<!-- cross-domain synergy.</li>-->
<!-- <li><b>Domain-Specific Customization</b>: Specialized hardware design and-->
<!-- customized software algorithms will trade universality for superior-->
<!-- performance and efficiency in certain use domains.</li>-->
<!-- <li><b>Cross-Layer Co-design</b>: Hardware (device, circuit, architecture) and-->
<!-- software-->
<!-- (algorithm, application) will be synergistically designed/optimized and converge-->
<!-- to-->
<!-- better Patero frontier. Sensing, computing, and interconnects will be-->
<!-- co-designed in-->
<!-- the system level.</li>-->
<!-- <li><b>Agile & Intelligent Design Automation</b>: AI-Enhanced automation flow for-->
<!-- productive, adaptive, and high-quality design and deployment pipeline. We want-->
<!-- to-->
<!-- close the virtuous cycle of emerging hardware for AI/ML and AI/ML for hardware-->
<!-- design automation. We are specifically enthusiastic about electronic-photonic-->
<!-- design-->
<!-- automation (EPDA).</li>-->
<!-- </p>-->
<!-- <h4><b style="color: #EA6B66">Hardware Design -- Emerging Technology and Customized Device/Circuit/Architecture</b>-->
<!-- </h4>-->
<!-- <p><b>Papers:</b> -->
<!-- <a href="https://doi.org/10.1109/TCAD.2020.3027649", style="color: #EA6B66">[FFT-ONN, TCAD'21]</a>, -->
<!-- <a href="https://doi.org/10.23919/DATE51398.2021.9474203", style="color: #EA6B66">[O<sup>2</sup>NN, DATE'21]</a>,-->
<!-- <a href="https://pubs.acs.org/doi/abs/10.1021/acsphotonics.2c01188", style="color: #EA6B66">[OSNN, ACS Photonics'22]</a>, -->
<!-- <a href="https://doi.org/10.1109/TCAD.2022.3189567", style="color: #EA6B66">[SqueezeLight, TCAD'22]</a>, -->
<!-- <a href="https://arxiv.org/abs/2208.08099", style="color: #EA6B66">[Fuse and Mix, ICCAD'22]</a>, -->
<!-- <a-->
<!-- href="https://arxiv.org/abs/2107.10845", style="color: #EA6B66">[QuantumNAS, HPCA'22]</a>,-->
<!-- <a href="https://arxiv.org/abs/2305.19533", style="color: #EA6B66">[DOTA, MLSys SNAP'23]</a>, -->
<!-- <a href="https://arxiv.org/abs/2305.19505", style="color: #EA6B66">[M<sup>3</sup>ICRO, arXiv'23]</a> -->
<!-- </p>-->
<!-- <p>-->
<!-- We are interested in emerging semiconductor technology for sensing, computing, and-->
<!-- interconnects tasks, on both foundation-level device/circuit and higher-level-->
<!-- architecture systems.-->
<!-- We design heterogeneous electronic-photonic computing systems using optics, CMOS, as-->
<!-- well-->
<!-- as post-CMOS electronics, which we believe will be transformative technologies to-->
<!-- break-->
<!-- through the compute density, energy efficiency, and speed limitations of current-->
<!-- computing solutions.-->
<!-- We will push the state-of-the-art emerging computing system by application-specific-->
<!-- customization and broaden its practical applicability with superior robsutness,-->
<!-- performance, efficiency, and adaptability.-->
<!-- </p>-->
<!-- <h4><h4><b style="color: #67AB9F">Design Automation Flow -- Intelligent Design Automation Toolkits</b></h4>-->
<!-- <p><b>Papers:</b> -->
<!-- <a href="https://doi.org/10.1109/TCAD.2020.3003843" style="color: #67AB9F">[DREAMPlace, TCAD'20]</a>,-->
<!-- <a href="https://doi.org/10.1145/3400302.3415691" style="color: #67AB9F">[DREAMPlace 3.0, ICCAD'20]</a>,-->
<!-- <a href="https://doi.org/10.23919/DATE48585.2020.9116330" style="color: #67AB9F">[Layout Pred., DATE'20]</a>,-->
<!-- <a href="https://qmlsys.mit.edu/wp-content/uploads/2022/09/ICCAD2022_Transformer.pdf" style="color: #67AB9F">[Quantum Pred, ICCAD'22]</a>,-->
<!-- <a href="https://arxiv.org/abs/2112.08703" style="color: #67AB9F">[ADEPT, DAC'22]</a>, -->
<!-- <a href="https://doi.org/10.1145/3489517.3530597" style="color: #67AB9F">[Timing Pred, DAC'22]</a>,-->
<!-- <a href="http://arxiv.org/abs/2209.10098" style="color: #67AB9F">[NeurOLight, NeurIPS'22]</a>-->
<!-- </p>-->
<!-- <p>-->
<!-- We develop end-to-end electronic-photonic design automation toolchains based on-->
<!-- optimization and AI-assisted methodologies, including simulation, layout,-->
<!-- performance evaluation, and-->
<!-- automatic design space exploration. We are interested in new problem formulation and-->
<!-- novel solutions based on various optimization techniques and high-performance kernel-->
<!-- implementation.-->
<!-- We believe a fully-automated design flow that supports incremental interactive-->
<!-- design pipelines can maximize the design quality, productivity, and efficiency when-->
<!-- advancing-->
<!-- emerging hardware platforms.-->
<!-- AI/ML for hardware design automation is a specific topic our group is interested in,-->
<!-- where customized ML algorithms will be introduced and adapted to solve challenging-->
<!-- problems and break the limitation of conventional optimization/heuristics-based-->
<!-- design automation paradigm.-->
<!-- </p>-->
<!-- <h4><b style="color: #0081CC">Efficient Algorithms -- Cross-Layer Co-design and Advanced AI/ML Applications</b></h4>-->
<!-- <p><b>Papers:</b> -->
<!-- <a href="https://doi.org/10.23919/DATE48585.2020.9116521" style="color: #0081CC">[ROQ, DATE'20]</a>,-->
<!-- <a href="https://doi.org/10.1109/DAC18072.2020.9218593" style="color: #0081CC">[FLOPS, DAC'20]</a>, -->
<!-- <a href="https://arxiv.org/abs/2012.11148" style="color: #0081CC">[MixedTrain, AAAI'21]</a>,-->
<!-- <a href="https://arxiv.org/abs/2108.11430" style="color: #0081CC">[MemEff-ONN, ICCV'21]</a>,-->
<!-- <a href="https://arxiv.org/abs/2110.14807" style="color: #0081CC">[L<sup>2</sup>ight, NeurIPS'21]</a>,-->
<!-- <a href="https://doi.org/10.1109/TCAD.2022.3180969" style="color: #0081CC">[ELight, TCAD'22]</a>,-->
<!-- <a href="https://arxiv.org/abs/2110.11331" style="color: #0081CC">[QuantumNAT, DAC'22]</a>,-->
<!-- <a href="https://arxiv.org/abs/2202.13239" style="color: #0081CC">[QOC, DAC'22]</a>,-->
<!-- <a href="https://arxiv.org/abs/2111.01236" style="color: #0081CC">[HRViT, CVPR'22]</a>,-->
<!-- <a href="https://arxiv.org/abs/2211.16749" style="color: #0081CC">[HEAT, NeurIPS MLSys'22]</a>-->
<!-- </p>-->
<!-- <p>-->
<!-- To demonstrate real-world impact of emerging computing systems, we are interested in-->
<!-- various emerging application of our developed hardware, including neural network-->
<!-- inference/training on cloud and edge, scientific computing, optimization, robotics,-->
<!-- IoT,-->
<!-- autonomous driving, UAV, health, and other inter-disciplinary and cross-disciplinary-->
<!-- fields.-->
<!-- To enable reliable and efficient deployment on those use domain, cross-layer-->
<!-- co-design methodologies and advanced machine learning algorithms will be explored to-->
<!-- achieve digital-comparable task-->
<!-- performance (e.g., accuracy), high robustness (e.g., against noises/attacks), and-->
<!-- high-->
<!-- efficiency (e.g., power/energy/area).-->
<!-- </p>-->
<!-- <br> -->
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<h2>Datasets</h2>
</div>
<div class="trend-entry d-flex">
<div class="trend-contents"> -->
<!-- <ul>
<li>IEEE TIP'23, Multi-Purpose Real Haze Benchmark (<b>A2I2-Haze</b>) dataset <a href=" https://a2i2-archangel.vision">[Webpage]</a></li>
<li>IEEE TPAMI'20 + ECCV'18, Privacy attributes in HMDB51 (<b>PA-HMDB51</b>) dataset <a href="https://htwang14.github.io/PA-HMDB51-website/index.html">[Webpage]</a></li>
<li>IJCV'20 + CVPR'19, Multi-Purpose Image Deraining (<b>MPID</b>) dataset <a href="https://github.com/lsy17096535/Single-Image-Deraining">[Webpage]</a></li>
<li>IEEE TIP'19, REalistic Single Image DEhazing (<b>RESIDE</b>) dataset <a href="https://sites.google.com/view/reside-dehaze-datasets">[Webpage]</a></li>
<li>ACM MM'15, Adobe Visual Font Recognition (<b>VFR</b>) dataset <a href="https://drive.google.com/drive/folders/1VJ8rlmxWjS2Xv0eFECfpRuNWuRrqh6h6?usp=sharing">[Webpage]</a></li>
</ul> -->
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