Design Space Exploration of an In-Sensor Processor for Object Classification in Ultra-compact Time-of-Flight Sensors

This work presents an extensive design space exploration toward the optimal design of a hardware (HW) accelerator for multiclass object classification, implementing a configurable convolutional neural network (CNN) to be closely coupled with ultralow spatial resolution (ULR) time-of-flight (ToF) sensors in an in-sensors computing approach. The study leverages ULR ToF as the sole sensing element to perform classification, exploiting only the low-resolution depth map of the scene. The investigation, based on the STMicroelectronics VL53L8CX 8 x 8 pixel ToF sensor, led to very high accuracy, together with unprecedented low power consumption, compactness, and real-time operation. Indeed, the CNN classifies four objects even in the presence of partial occlusion and overlap, achieving an accuracy higher than 92% when an 8-bit posttraining quantization is used. The derived architecture, implemented in skywater CMOS 130-nm technology, occupies less than 1 mm(2) of area, with a power consumption of about 11% of the overall power consumption of the sensor in ranging mode and consumes less than 80 nJ per inference with an inference time of 3.3 mu s.