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

This work presents an extensive design space exploration towards the optimal design of an 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 8×8 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 post-training quantization is used. The derived architecture, implemented in Skywater CMOS 130 nm technology, occupies less than 1 mm2 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 μs.