⚡ 本页包含 AI 生成的分析内容，仅供参考
本文提出一种受大脑启发的低功耗架构研究方法，通过同时优化工艺、电路、系统架构和学习算法，以满足物联网边缘设备的嵌入式系统需求。论文综述了从器件到算法的跨层次协同设计思路，旨在实现类脑芯片。
Abstract The advent of the Internet-of-Things has introduced a new paradigm that supports a decentralized and hierarchical communication architecture, where a great deal of analytics processing occurs at the edge and at the end-devices instead of in the Cloud. To map the embedded-systems requirements, we present a holistic research approach to the development of low-power architectures inspired by the human brain, where process development and integration, circuit design, system architecture, and learning algorithms are simultaneously optimized. This paper is organized as follows: We begin with a survey of recent research on the human brain and a historical perspective of cognitive neuroscience. Then, artificial intelligence is introduced, and the challenges of Deep Learning systems (in terms of power requirements) are addressed. The key reasons to distribute intelligence over the whole network are discussed. To emphasize the need for low-power
Barbara De Salvo, Chief Scientist and Scientific Director
CEA-Leti, Université Grenoble Alpes, France