The AI chips increasingly focus on implementing neural computing at low power and cost. The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. Increasingly, the generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilities. Such a requirement to transit from application-specific to general intelligence AI chip must consider several factors. This paper provides an overview of this cross-disciplinary field of study, elaborating on the generalisation of intelligence as understood in building artificial general intelligence (AGI) systems. This work presents a listing of emerging AI chip technologies, classification of edge AI implementations, and the funnel design flow for AGI chip development. Finally, the design consideration required for building an AGI chip is listed along with the methods for testing and validating it.
翻译：人工智能芯片越来越多地侧重于以低电费和低成本实施神经计算。智能感测、自动化和边缘计算应用一直是人工智能芯片的市场驱动力。AI芯片解决方案的概括性、性能、稳健性和可伸缩性日益与人性智能能力相比较。这种从专门应用到一般情报AI芯片的中转要求必须考虑若干因素。本文件概述了这一跨学科研究领域,详细阐述了在建立人工一般智能系统时所理解的情报的普及性。这项工作列出了新兴的人工智能芯片技术、边缘人工智能实施分类以及用于AGI芯片开发的漏斗设计流程。最后,将建立AGI芯片所需的设计考虑与测试和验证方法一起列出。
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