Rising connectivity in vehicles is enabling new capabilities like connected autonomous driving and advanced driver assistance systems (ADAS) for improving the safety and reliability of next-generation vehicles. This increased access to in-vehicle functions compromises critical capabilities that use legacy invehicle networks like Controller Area Network (CAN), which has no inherent security or authentication mechanism. Intrusion detection and mitigation approaches, particularly using machine learning models, have shown promising results in detecting multiple attack vectors in CAN through their ability to generalise to new vectors. However, most deployments require dedicated computing units like GPUs to perform line-rate detection, consuming much higher power. In this paper, we present a lightweight multi-attack quantised machine learning model that is deployed using Xilinx's Deep Learning Processing Unit IP on a Zynq Ultrascale+ (XCZU3EG) FPGA, which is trained and validated using the public CAN Intrusion Detection dataset. The quantised model detects denial of service and fuzzing attacks with an accuracy of above 99 % and a false positive rate of 0.07%, which are comparable to the state-of-the-art techniques in the literature. The Intrusion Detection System (IDS) execution consumes just 2.0 W with software tasks running on the ECU and achieves a 25 % reduction in per-message processing latency over the state-of-the-art implementations. This deployment allows the ECU function to coexist with the IDS with minimal changes to the tasks, making it ideal for real-time IDS in in-vehicle systems.