Memory-Based Computing for Energy-Efficient AI: Grand Challenges
Memory-Based Computing for Energy-Efficient AI: Grand Challenges
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DOI：
10.1109/vlsi-soc57769.2023.10321880
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Foroozan Karimzadeh;Mohsen Imani;Bahar Asgari;Ningyuan Cao;Yingyan Lin;Yan Fang
中科院分区：
文献类型：
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作者：
Foroozan Karimzadeh;Mohsen Imani;Bahar Asgari;Ningyuan Cao;Yingyan Lin;Yan Fang
人工智能(AI)的显著进步开启了一个以具有数十亿参数的模型为特征的新时代，使人们能够跨不同领域提供非凡的能力。然而，这些成就在内存和能源消耗方面付出了巨大的代价。对计算资源日益增长的需求对节能人工智能系统的可持续发展提出了巨大挑战。本文深入探讨了基于内存的计算范例，将其作为解决这些挑战的一种有前途的途径。通过利用内存的固有特性及其高效利用，基于内存的计算提供了一种新的方法来提高人工智能性能，同时降低相关的能源成本。我们的论文系统地分析了这一范式的多方面，突出了它的潜在好处，并概述了它所带来的挑战。通过对各种方法、体系结构和算法的探索，我们阐明了内存利用率、计算效率和AI模型复杂性之间复杂的相互作用。此外，我们还回顾了基于内存的计算的硬件和软件解决方案的发展领域，强调了它们对实现高能效人工智能系统的影响。随着人工智能继续快速发展，本文提出的关键挑战和见解为研究人员提供了基础性指南，帮助他们努力驾驭基于内存的计算这一复杂领域，以及它在塑造高能效人工智能未来中的关键作用。
The remarkable progress in artificial intelligence (AI) has ushered in a new era characterized by models with billions of parameters, enabling extraordinary capabilities across diverse domains. However, these achievements come at a significant cost in terms of memory and energy consumption. The growing demand for computational resources raises grand challenges for the sustainable development of energy-efficient AI systems. This paper delves into the paradigm of memory-based computing as a promising avenue to address these challenges. By capitalizing on the inherent characteristics of memory and its efficient utilization, memory-based computing offers a novel approach to enhance AI performance while reducing the associated energy costs. Our paper systematically analyzes the multifaceted aspects of this paradigm, highlighting its potential benefits and outlining the challenges it poses. Through an exploration of various methodologies, architectures, and algorithms, we elucidate the intricate interplay between memory utilization, computational efficiency, and AI model complexity. Furthermore, we review the evolving area of hardware and software solutions for memory-based computing, underscoring their implications for achieving energy-efficient AI systems. As AI continues its rapid evolution, identifying the key challenges and insights presented in this paper serve as a foundational guide for researchers striving to navigate the complex field of memory-based computing and its pivotal role in shaping the future of energy-efficient AI.