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Data Reduction Techniques for Simulation, Visualization and Data Analysis by Li, S., Marsaglia, N., Garth, C., Woodring, J., Clyne, J., Childs, H.

“…Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used…”
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Base-delta-immediate compression: Practical data compression for on-chip caches by Pekhimenko, Gennady, Seshadri, Vivek, Mutlu, Onur, Kozuch, Michael A., Gibbons, Phillip B., Mowry, Todd C.

“…Cache compression is a promising technique to increase on-chip cache capacity and to decrease on-chip and off-chip bandwidth usage. Unfortunately, directly…”
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Quantum Neural Network Compression by Hu, Zhirui, Dong, Peiyan, Wang, Zhepeng, Lin, Youzuo, Wang, Yanzhi, Jiang, Weiwen

“…Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there…”
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Faster and Stronger Lossless Compression with Optimized Autoregressive Framework by Mao, Yu, Li, Jingzong, Cui, Yufei, Xue, Jason Chun

“…Neural AutoRegressive (AR) framework has been applied in general-purpose lossless compression recently to improve compression performance. However, this paper…”
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NN-AdderNet: Nonnegative and Sparse Weight Optimization Towards Ultra-Low Bitwidth AdderNet Quantization and Compression by Zhang, Yunxiang, Sun, Gengchen, Fang, Lizhi, Sun, Biao, Zhao, Wenfeng

“…Emerging efficient deep neural network (DNN) models, such as AdderNet, have shown great promise in significantly improving hardware efficiency compared to…”
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PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning by Wang, Yisu, Wu, Ruilong, Li, Xinjiao, Kutscher, Dirk

“…Large-scale deep neural networks (DNN) exhibit excellent performance for various tasks. As DNNs and datasets grow, distributed training becomes extremely…”
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BLOOM: Bit-Slice Framework for DNN Acceleration with Mixed-Precision by Liu, Fangxin, Yang, Ning, Wang, Zongwu, Zhu, Xuanpeng, Yao, Haidong, Xiong, Xiankui, Jiang, Li, Guan, Haibing

“…Deep neural networks (DNNs) have revolutionized numerous AI applications, but their vast model sizes and limited hardware resources present significant…”
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BirdMoE: Reducing Communication Costs for Mixture-of-Experts Training Using Load-Aware Bi-random Quantization by Wu, Donglei, Yang, Weihao, Zou, Xiangyu, Jia, Jinda, Tao, Dingwen, Xia, Wen, Tian, Zhihong

“…Mixture-of-Experts (MoE) model parallelism is prevalent in training Large Language Models (e.g., ChatGPT). However, the intensive all-to-all collective…”
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MemSens: Significantly Reducing Memory Overhead in Adjoint Sensitivity Analysis Using Novel Error-Bounded Lossy Compression by Li, Chenxi, Feng, Yihang, Deng, Fuxing, Tao, Dingwen, Liu, Weifeng, Jin, Zhou

“…Adjoint sensitivity analysis is an exceptionally efficient method for computing the gradient of an objective function with respect to given parameters, playing…”
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PISA: Efficient Precision-Slice Framework for LLMs with Adaptive Numerical Type by Yang, Ning, Wang, Zongwu, Sun, Qingxiao, Lu, Liqiang, Liu, Fangxin

“…Large language models (LLMs) have transformed numerous AI applications, with on-device deployment becoming increasingly important for reducing cloud computing…”
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SNAPPIX: Efficient-Coding-Inspired In-Sensor Compression for Edge Vision by Lin, Weikai, Ma, Tianrui, Boloor, Adith, Feng, Yu, Xing, Ruofan, Zhang, Xuan, Zhu, Yuhao

“…Energy-efficient image acquisition on the edge is crucial for enabling remote sensing applications where the sensor node has weak compute capabilities and must…”
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ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression by Liu, Guangda, Li, Chengwei, Zhao, Jieru, Zhang, Chenqi, Guo, Minyi

“…Large Language Models (LLMs) have been widely deployed in a variety of applications, and the context length is rapidly increasing to handle tasks such as…”
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DCDiff: Enhancing JPEG Compression via Diffusion-based DC Coefficients Estimation by Zhang, Ziyuan, Qiu, Han, Zhang, Tianwei, Chen, Bin, Zhang, Chao

“…JPEG is the most widely-used image compression method on low-cost cameras which cannot support learning-based compressors. One promising approach to enhance…”
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Late Breaking Results: Less Sense Makes More Sense: In-Sensor Compressive Learning for Efficient Machine Vision by Liang, Yiwen, Cao, Weidong

“…Integrating deep learning and image sensors has significantly transformed machine vision applications. Yet, conventional highresolution image acquisition…”
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CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning by Basit, Abdul, Nawaz, Maha, Rehman, Saim, Shafique, Muhammad

“…Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing capabilities-including pre-filtering,…”
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APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-Design by Tan, Yonghao, Dong, Pingcheng, Wu, Yongkun, Liu, Yu, Liu, Xuejiao, Luo, Peng, Liu, Shih-Yang, Huang, Xijie, Zhang, Dong, Liang, Luhong, Cheng, Kwang-Ting

“…DNN accelerators, significantly advanced by model compression and specialized dataflow techniques, have marked considerable progress. However, the frequent…”
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Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs by Mao, Yu, Li, Jingzong, Wang, Jun, Xu, Hong, Kuo, Tei-Wei, Guan, Nan, Xue, Chun Jason

“…Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in…”
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MILLION: MasterIng Long-Context LLM Inference Via Outlier-Immunized KV Product QuaNtization by Wang, Zongwu, Xu, Peng, Liu, Fangxin, Hu, Yiwei, Sun, Qingxiao, Li, Gezi, Li, Cheng, Wang, Xuan, Jiang, Li, Guan, Haibing

“…Large language models (LLMs) are increasingly utilized for complex tasks requiring longer context lengths, with some models supporting up to 128 K or 1 M…”
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KVO-LLM: Boosting Long-Context Generation Throughput for Batched LLM Inference by Li, Zhenyu, Lyu, Dongxu, Wang, Gang, Chen, Yuzhou, Chen, Liyan, Li, Wenjie, Jiang, Jianfei, Sun, Yanan, He, Guanghui

“…With the widespread deployment of long-context large language models (LLMs), efficient and high-quality generation is becoming increasingly important. Modern…”
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DuQTTA: Dual Quantized Tensor-Train Adaptation with Decoupling Magnitude-Direction for Efficient Fine-Tuning of LLMs by Dong, Haoyan, Chen, Hai-Bao, Chang, Jingjing, Yang, Yixin, Gao, Ziyang, Ji, Zhigang, Wang, Runsheng, Huang, Ru

“…Recent parameter-efficient fine-tuning (PEFT) techniques have enabled large language models (LLMs) to be efficiently fine-tuned for specific tasks, while…”
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