PDF(9787 KB)
PDF(9787 KB)
PDF(9787 KB)
芯粒功能划分方法与互连体系综述
Chipet functional partitioning and interconnection review
目前,芯片设计面临“面积墙”的挑战,这为芯片制造带来了高昂的流片成本。芯粒技术可以通过成熟的工艺制程制造较小面积的芯片,然后通过先进封装方式打破面积墙的限制,实现芯片的敏捷设计,降低设计成本。而设置多大的芯粒颗粒度可以满足芯片设计的灵活需求,是利用芯粒技术的一个核心问题。芯粒功能的划分也影响着芯粒间的互连结构,如何实现各功能芯粒间互连是最终实现芯片功能的关键。因此,本文综述国内外近年来对芯粒功能划分上的研究、在芯粒设计空间上的探索以及芯粒功能划分对芯粒间互连网络影响,并指出芯粒的设计方法学是未来芯粒技术发展的重要研究方向。
Facing the challenge of the "area wall" in chip design,there is a significant increase in chip manufacturing costs.The chiplet technology enables the production of small area chips using a mature process,and composing by advanced packaging techniques,which can overcome the limitations imposed by the area wall,facilitating agile chip design and reducing overall design costs.Determining an optimal chiplet particle size to meet flexible chip design requirements remains a crucial issue when utilizing chiplet technology.Furthermore,achieving interconnectivity between functional chiplets after dividing chip functions is pivotal for realizing the final functionality of the chip.Therefore,this paper provides a comprehensive review of recent research on chiplet function division,spatial exploration in chiplet design and the influence of chiplet function division on the inter-chip interconnect,while also pointing out that chipet design methodology is an important research direction for the development of chiplet technology in the future.
芯粒 / 芯粒功能颗粒度 / 芯粒间互连 / AMD / SiP
Chiplet / Chiplet particle / interconnectivity of chiplets / AMD / SiP
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Processing systems are in continuous evolution thanks to the constant technological advancement and architectural progress. Over the years, computing systems have become more and more powerful, providing support for applications, such as Machine Learning, that require high computational power. However, the growing complexity of modern computing units and applications has had a strong impact on power consumption. In addition, the memory plays a key role on the overall power consumption of the system, especially when considering data-intensive applications. These applications, in fact, require a lot of data movement between the memory and the computing unit. The consequence is twofold: Memory accesses are expensive in terms of energy and a lot of time is wasted in accessing the memory, rather than processing, because of the performance gap that exists between memories and processing units. This gap is known as the memory wall or the von Neumann bottleneck and is due to the different rate of progress between complementary metal–oxide semiconductor (CMOS) technology and memories. However, CMOS scaling is also reaching a limit where it would not be possible to make further progress. This work addresses all these problems from an architectural and technological point of view by: (1) Proposing a novel Configurable Logic-in-Memory Architecture that exploits the in-memory computing paradigm to reduce the memory wall problem while also providing high performance thanks to its flexibility and parallelism; (2) exploring a non-CMOS technology as possible candidate technology for the Logic-in-Memory paradigm.
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