Wan Tianqing, Shao Bangjie, Ma Sijie, Zhou Yue, Li Qiao, Chai Yang
Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China.
Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China.
Adv Mater. 2023 Sep;35(37):e2203830. doi: 10.1002/adma.202203830. Epub 2022 Jul 9.
The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in-sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in-sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in-sensor computing is provided.
物联网中传感器节点的数量正在迅速增长,导致传感终端产生大量数据。传感器与计算单元之间频繁的数据传输在能源效率、速度和安全性方面对系统性能造成了严重限制。为了高效处理大量传感数据,应开发一种能够将计算功能集成到传感器网络中的新型计算范式。传感器内计算范式减少了数据传输,还通过在本地处理数据降低了高计算复杂度。在此,讨论了传感器内计算范式在器件和阵列层面的硬件实现。阐述了导致独特传感响应特性及其相应计算功能的物理机制。特别地,受生物启发的器件特性使得神经形态计算功能得以实现。还讨论了集成技术,并对传感器内计算的未来发展提供了展望。