A low-cost in-tire-pressure monitoring SoC using integer/floating-point type convolutional neural network inference engine

Abstract

Tire pressure state is crucial for driving performance and fuel efficiency and is the welfare of the driver. Tire inflation pressure greatly affects fuel consumption, driving dynamics and the tire lifespan. Hence, the state of tire pressure must be continuously sensed for comfort riding, optimal vehicle handling, and safety, and it is necessary for all types of transport. The existing detection methods are high cost and without any redundancy, if the sensor gets fails, which retard its wide application. The support vector machine (SVM) learning method is introduced for tire pressure loss detection. But, this method is not so accurate in detecting the normal and pressure loss judgement of tires. This research presents a novel approach for a tire pressure monitoring system (TPMS) to reduce the cost of sensor-based frames. The proposed model comes under three stages: pre-processing, feature extraction and tier pressure detection. Initially, an adaptive and unbiased Kalman filter (KF) with a recursive least square (RLS) algorithm (AUK-RLS) approach is introduced to eliminate manufacturing errors in speed gear. The second stage is the extraction of features. Statistical, frequency domain, and fitted frequency domain characteristics features are extracted from the wheel speed signal in this stage. It is fed to the deep learning (DL) model to perform the judgement of tier state. While using floating point model for the operation and storage process, the hardware cost of the DL model tends to be high. To tackle this issue, the convolutional neural network (CNN) hardware design will be optimized by combining both Integer/Floating-Point type inference engines. The design modules of the CNN interference engine are insisted of buffers, on-chip/off-chip memories, controllers, fine state machine (FSM), processing engine (PE) and routers. The PE module needs several adders and multiplier units, and this performs the convolution operation. Therefore, to diminish the area, delay and power consumption, the modified adder and multiplier design named Vedic multiplier based Carry select adder with simplified combinational logics (CSLA-SCL) is emphasized. The proposed system is implemented using Verilog code synthesis in the Xilinx ISE 14.5 simulator platform. The performance measure of pressure loss detection in terms of accuracy (99.25%) and errors are examined. Also, the performance measure of the detection model with the proposed adder and multiplier design is examined in terms of area utilization, power (145 mW), frequency (756 MHz) and delay (7.12 ns).