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 × 10−17 kWh and 17.79 × 10−19 kWh 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.

This paper presents integrated silicon-based material that can be configured to sense, perform different classification algorithms through neural computing, and produce an action signal all at the same physical layer. The algorithms will be coded in the mechanical responses of the sensing elements of multiple coupled micro-electro-mechanical systems (MEMS) that also simultaneously capture acceleration measurements to produce an actuated signal. This all-in-one smart material consumes near zero power and runs with zero circuitry. As a demonstration, a material made of three MEMS neurons is designed and fabricated to perform successfully both simple signal classification and activity recognition problems. This smart material will enable emergent technologies such as soft robotics and wearable devices to locally perform complex computations powered by permanent batteries.