Energy efficient integrated MEMS neural network for simultaneous sensing and computing
Energy efficient integrated MEMS neural network for simultaneous sensing and computing
复制标题
用于同步传感和计算的节能集成 MEMS 神经网络
DOI：
10.1038/s44172-023-00071-6
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Alsaleem, Fadi
中科院分区：
文献类型：
--
作者：
Nikfarjam, Hamed;Megdadi, Mohammad;Okour, Mohammad;Pourkamali, Siavash;Alsaleem, Fadi
生物系统将多种功能无缝地联合收割机结合在轻质和节能的结构中。合成结构中的这种能力在许多工程应用中是期望的，例如航空航天、机器人和可穿戴设备。在这里，我们报告了一个集成的硅基结构，配置为感测，执行不同的分类算法，并在同一物理层内产生一个动作信号。该算法被编码在多个耦合的微机电系统（MEMS）的传感元件的机械响应中，同时捕获加速度测量以产生致动信号。这种一体化结构以零电路和低功耗运行。作为演示，我们设计并制造了一个由三个MEMS神经元组成的网络，成功地完成了简单的信号分类和活动识别问题（站立和坐下），每次操作的能耗分别为9.92 × 10−17kWh和17.79 × 10−19kWh。我们的方法将使新兴技术，如可穿戴设备，能够通过一次电池充电来执行复杂的计算。
Biological systems seamlessly combine multiple functions in lightweight and energy-efficient structures. Such capability in synthetic structures would be desirable in numerous engineering applications such as aerospace, robotics and wearable devices. Here we report an integrated silicon-based structure configured to sense, perform different classification algorithms, and produce an action signal within the same physical layer. The algorithms are coded in the mechanical responses of the sensing elements of multiple coupled micro-electro-mechanical systems (MEMS), simultaneously capturing acceleration measurements to produce an actuated signal. This all-in-one structure operates with zero circuitry and low power consumption. As a demonstration, we designed and fabricated a network of three MEMS neurons to successfully perform both simple signal classification and activity recognition problems (standing and sitting) with only 9.92 × 10−17kWh and 17.79 × 10−19kWh energy consumption per operation, respectively. Our approach will enable emergent technologies, such as wearable devices, to perform complex computations with power from a single battery charge.
影响因子：
64.8
作者：
Yasuda, Hiromi;Buskohl, Philip R.;Raney, Jordan R.
通讯作者：
Raney, Jordan R.
影响因子：
4.8
作者：
Yu X;Jang J;Xiong S
通讯作者：
Xiong S
影响因子：
64.8
作者：
El Helou, Charles;Grossmann, Benjamin;Harne, Ryan L.
通讯作者：
Harne, Ryan L.