Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array - Di Lu; Wenchang Li; Jian Liu; Gang Chen; Zhigang Li (Applied Sciences, 2023)
Spiking neural networks inspired by biological models are gaining popularity in artificial intelligence due to their ability to solve diverse problems while reducing energy consumption. As a result of the trade-off between the need to transmit large amounts of data and the power consumption of hardware deployment, artificial vision systems are particularly well-suited to construction using spiking neural networks (SNNs). How to communicate with the neuromorphic network effectively is one of the challenges associated with building systems that utilize SNN systems. It is necessary to convert the data to spike form before they can be processed by an SNN as input, unless neuromorphic or event-triggered sensing systems are employed. We present a configurable circuit based on a focal plane array (FPA) capable of providing spike-encoded readout data at the pixel level. With this type of circuit, the current signal of the photoelectric sensor can be encoded into two spike encodings with different precision, which are sent for processing to SNNs. This provides image information at two different scales for the artificial vision system based on SNNs. With this feature, we can use this circuit and different SNN structures to build an artificial target recognition system that is closer to the biological visual system.