- AI-ANNE: 将神经网络迁移到微控制器的深度探索Klinkhammer D. AI-ANNE:(A)(N) eural (N) et for (E) xploration: Transferring Deep Learning Models onto Microcontrollers and Embedded Systems[J]. arXiv preprint arXiv:2501.... AI-ANNE: 将神经网络迁移到微控制器的深度探索Klinkhammer D. AI-ANNE:(A)(N) eural (N) et for (E) xploration: Transferring Deep Learning Models onto Microcontrollers and Embedded Systems[J]. arXiv preprint arXiv:2501....
- 边缘AI优化:数据、模型与系统策略的综合调研——论文阅读Wang X, Jia W. Optimizing edge AI: a comprehensive survey on data, model, and system strategies[J]. arXiv preprint arXiv:2501.03265, 2025. 第一章 引言与研究背景 1.1 研究动机与挑战人工智能技术... 边缘AI优化:数据、模型与系统策略的综合调研——论文阅读Wang X, Jia W. Optimizing edge AI: a comprehensive survey on data, model, and system strategies[J]. arXiv preprint arXiv:2501.03265, 2025. 第一章 引言与研究背景 1.1 研究动机与挑战人工智能技术...
- I-ViT: 用于高效视觉Transformer推理的纯整数量化Li Z, Gu Q. I-vit: Integer-only quantization for efficient vision transformer inference[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.... I-ViT: 用于高效视觉Transformer推理的纯整数量化Li Z, Gu Q. I-vit: Integer-only quantization for efficient vision transformer inference[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision....
- 模型量化技术简要详解 模型量化的本质与基础原理模型量化技术本质上是一种精度与效率的权衡艺术。想象一下,如果我们用数字来记录一个房间的温度,使用小数点后十位的精度(如23.1234567890°C)虽然非常精确,但在日常生活中,精确到小数点后一位(23.1°C)就足够了。模型量化的核心思想与此类似——将神经网络中的高精度浮点数(通常是32位浮点数,FP32)转换为低精度的整数表示(如8位整数... 模型量化技术简要详解 模型量化的本质与基础原理模型量化技术本质上是一种精度与效率的权衡艺术。想象一下,如果我们用数字来记录一个房间的温度,使用小数点后十位的精度(如23.1234567890°C)虽然非常精确,但在日常生活中,精确到小数点后一位(23.1°C)就足够了。模型量化的核心思想与此类似——将神经网络中的高精度浮点数(通常是32位浮点数,FP32)转换为低精度的整数表示(如8位整数...
- MicroNAS:面向MCU的零样本神经架构搜索Qiao Y, Xu H, Zhang Y, et al. Micronas: Zero-shot neural architecture search for mcus[C]//2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 202... MicroNAS:面向MCU的零样本神经架构搜索Qiao Y, Xu H, Zhang Y, et al. Micronas: Zero-shot neural architecture search for mcus[C]//2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 202...
- H4H:面向AR/VR应用的NPU-CIM异构系统混合卷积-Transformer架构搜索Yiwei Zhao, Jinhui Chen, Sai Qian Zhang, Syed Shakib Sarwar, Kleber Hugo Stangherlin, Jorge Tomas Gomez, Jae-Sun Seo, Barbara De Salvo, Chiao Liu, Phil... H4H:面向AR/VR应用的NPU-CIM异构系统混合卷积-Transformer架构搜索Yiwei Zhao, Jinhui Chen, Sai Qian Zhang, Syed Shakib Sarwar, Kleber Hugo Stangherlin, Jorge Tomas Gomez, Jae-Sun Seo, Barbara De Salvo, Chiao Liu, Phil...
- SmoothQuant: 大型语言模型的精确高效后训练量化Xiao G, Lin J, Seznec M, et al. Smoothquant: Accurate and efficient post-training quantization for large language models[C]//International conference on machine learni... SmoothQuant: 大型语言模型的精确高效后训练量化Xiao G, Lin J, Seznec M, et al. Smoothquant: Accurate and efficient post-training quantization for large language models[C]//International conference on machine learni...
- 用于最近邻搜索的乘积量化H. Jégou, M. Douze and C. Schmid, “Product Quantization for Nearest Neighbor Search,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 117-128, Ja... 用于最近邻搜索的乘积量化H. Jégou, M. Douze and C. Schmid, “Product Quantization for Nearest Neighbor Search,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 117-128, Ja...
- 改进的激光方法与更快的矩阵乘法Josh Alman and Virginia Vassilevska Williams. 2021. A refined laser method and faster matrix multiplication. In Proceedings of the Thirty-Second Annual ACM-SIAM Symposium on Discret... 改进的激光方法与更快的矩阵乘法Josh Alman and Virginia Vassilevska Williams. 2021. A refined laser method and faster matrix multiplication. In Proceedings of the Thirty-Second Annual ACM-SIAM Symposium on Discret...
- 使用分区截断奇异值分解滤波的近似卷积J. Atkins, A. Strauss and C. Zhang, “Approximate convolution using partitioned truncated singular value decomposition filtering,” 2013 IEEE International Conference on Acoustics,... 使用分区截断奇异值分解滤波的近似卷积J. Atkins, A. Strauss and C. Zhang, “Approximate convolution using partitioned truncated singular value decomposition filtering,” 2013 IEEE International Conference on Acoustics,...
- 无乘法器的多常数乘法Yevgen Voronenko and Markus Püschel. 2007. Multiplierless multiple constant multiplication. ACM Trans. Algorithms 3, 2 (May 2007), 11–es. 第一章 引言与问题定义在数字信号处理(DSP)和计算机算术领域,一个核心问题是如何高效地计算变量... 无乘法器的多常数乘法Yevgen Voronenko and Markus Püschel. 2007. Multiplierless multiple constant multiplication. ACM Trans. Algorithms 3, 2 (May 2007), 11–es. 第一章 引言与问题定义在数字信号处理(DSP)和计算机算术领域,一个核心问题是如何高效地计算变量...
- EdgeShard:通过协作边缘计算实现高效的大语言模型推理M. Zhang, X. Shen, J. Cao, Z. Cui and S. Jiang, “EdgeShard: Efficient LLM Inference via Collaborative Edge Computing,” in IEEE Internet of Things Journal, vol. 12, no... EdgeShard:通过协作边缘计算实现高效的大语言模型推理M. Zhang, X. Shen, J. Cao, Z. Cui and S. Jiang, “EdgeShard: Efficient LLM Inference via Collaborative Edge Computing,” in IEEE Internet of Things Journal, vol. 12, no...
- Agile-Quant:面向大语言模型边缘端更快推理的激活引导量化框架Shen X, Dong P, Lu L, et al. Agile-quant: Activation-guided quantization for faster inference of LLMs on the edge[C]//Proceedings of the AAAI Conference on Artif... Agile-Quant:面向大语言模型边缘端更快推理的激活引导量化框架Shen X, Dong P, Lu L, et al. Agile-quant: Activation-guided quantization for faster inference of LLMs on the edge[C]//Proceedings of the AAAI Conference on Artif...
- 在人工智能的发展过程中,AI Agent 已经逐渐从单一任务执行者演化为具备自主学习、协作和推理能力的智能体。在应对复杂决策场景(如智能制造、金融交易、灾害应急、智能交通)时,仅依赖单个 Agent 的计算与感知能力往往难以满足高效、鲁棒的决策需求。 在人工智能的发展过程中,AI Agent 已经逐渐从单一任务执行者演化为具备自主学习、协作和推理能力的智能体。在应对复杂决策场景(如智能制造、金融交易、灾害应急、智能交通)时,仅依赖单个 Agent 的计算与感知能力往往难以满足高效、鲁棒的决策需求。
- Mixture of Experts架构的简要解析 MoE的起源与核心思想Mixture of Experts(MoE)架构的历史可以追溯到1991年,当时Robert Jacobs、Michael Jordan、Geoffrey Hinton等人在《Neural Computation》期刊上发表了开创性论文《Adaptive Mixtures of Local Experts》。这篇论... Mixture of Experts架构的简要解析 MoE的起源与核心思想Mixture of Experts(MoE)架构的历史可以追溯到1991年,当时Robert Jacobs、Michael Jordan、Geoffrey Hinton等人在《Neural Computation》期刊上发表了开创性论文《Adaptive Mixtures of Local Experts》。这篇论...
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