Design of a VLSI Enabled Quantum-Resistant Blockchain Architecture with AI (Artificial Intelligence)–Driven Intrusion Detection System and Computer Vision for Secure Embedded, Internet of Things Edge Systems

The paper describes a new architecture, which closely incorporates VLSI-optimized post-quantum cryptography with blockchain, deep-learning-based intrusion detection, and computer vision to secure IoT-edge and autonomous vehicle networks. A hardware based implementation of a lattice-based crypto-engine (LUT-CLA-QTL design) with quantum-resistant authentication and ledger operation is proposed to be synthesized with 7-3 nm ASIC flows [1] [2]. These lattice signatures are implemented with a permissioned blockchain protocol to authenticate IoT devices and offer data integrity in resource-constrained edge networks. At the same time, an AI-based intrusion detection system (IDS) is implemented in the edge, using hybrid CNN-LSTM models, Graph Neural Networks (GNNs) and transformer-based models, which are quantized through the use of TensorFlow Lite or PyTorch Mobile to make inferences on-the-edge [3] [4]. Computer vision modules (e.g. object detectors based on YOLOv8) offer situational threat awareness (e.g. identifying illegal individuals or vehicles through smart cameras) and inject corresponding alerts into the blockchain. We used Cadence and Synopsys flows ( Cadence Genus/ Inno vus, Synopsys Design Compiler ) to verify ASIC/FPGA with the architecture. Multi-nanometer gains (e.g. 0.45 mm2 at 3 nm, 1.00 ns, 35 mW) and FPGA verification on Xilinx UltraScale+ (3500 LUTs, 3.2 ns, 90 mW) [7] [8] are demonstrated by the ASIC results (Table 1). Deep models give over 95% accuracy of IDS with standard IoT data, and YOLOv8 gives real-time detection accuracy (~80-90) on surveillance applications. The built-in system has strong authentication, low-latency encryption, and high detection, which is appropriate to secure autonomous vehicles and intelligent city IoT.