先进机器学习架构的能效和鲁棒性：一种跨层方法
Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems. This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.
English | 2025 | ISBN:9781032855509 | 361页| True PDF EPUB | 53.65 MB机器学习（ML）算法显示出了很高的准确性，应用程序在许多系统和平台中得到了广泛的应用。然而，开发高效的基于机器学习的系统需要解决三个问题：能源效率、鲁棒性和通常专注于优化单个目标/目标有限的技术。本书通过利用高级机器学习模型的独特功能来应对这些挑战，并研究了跨层概念和技术，以采用硬件和软件级方法为这些高级机器学习网络构建健壮且节能的架构。更具体地说，本书通过专门的硬件级设计和软件级优化流程，利用这些系统的应用驱动知识以及通过近似和量化的容错性，提高了CapsNets等复杂模型的能源效率。这本书还提高了机器学习模型的鲁棒性，特别是对于在神经形态硬件上执行的SNN，因为它们具有固有的成本效益特征。本书将多个优化目标整合到专门的框架中，以共同优化这些系统的鲁棒性和能效。这是计算机和电气工程专业的学生和研究人员的重要资源，他们有兴趣开发节能和强大的ML。本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。
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