## 4 Hardware Security
### Physical Unclonable Functions (PUFs)
#### 4.1.5 Integration of FPGAs-Based PUFs with Edge AI  
Artificial Intelligence (AI) assisted data analytics at the edge, allowing for improved interpretation of raw and unstructured data from the physical world. AI at the edge has the potential to automate complex and advanced tasks while preventing user-sensitive data from being transmitted over the network and into data centers at the same time. Edge AI models human reasoning, thus enabling machines to sense, comprehend, perform intelligent detection, and transmit results to the cloud for long-term storage or big data processing. It is capable of recognizing and fighting back against cyberattacks as well as other cyber threats based on the continuous input of data, identifying patterns, and backtracking the attacks. Data privacy and security breaches need to be taken seriously as they may cause business interruptions, revenue losses, and panic among the public [271]. The human brain comprises nearly 100 billion neurons, and over 100 trillion connections are established to form a network of neurons which in turn significantly influences the brain's capabilities. The interconnectivity within an FPGA resembles the neural wiring of the human brain, and its programmable logic fabric offers the flexibility of the brain [272]. The dynamically reconfigurable as well as customizable hardware architecture of Field Programmable Gate Arrays (FPGAs) has offered a promising solution in accelerating compute-intensive workloads [37]. FPGA-based edge network accelerators offload intelligence, data processing, analytics, and communication capabilities from the cloud to where the data originates [273]. Cloud computing provides the infrastructure needed for securing users' data as well as maintaining their integrity and privacy. However, there is no foolproof technique yet that guarantees data protection nor a processor that can isolate the execution of users' applications from data theft. FPGAs are capable of providing stronger security guarantees as there is no need to involve vulnerable operating systems, drivers, or compilers, nor any other system software [274]. The possibility of incorporating general-purpose processors such as soft cores on FPGAs makes these reconfigurable devices suitable for IoT applications as they can provide solutions with enhanced security, reduced size, energy consumption, and cost [275]. Silicon chip fabricators and designers have integrated FPGA and ARM processor cores for efficient edge AI processing. Also, the benefits of shorter development time make an FPGA-based solution the ideal choice for an intelligent edge device [276]. Integrated chip manufacturers mostly outsource their operations, where intellectual property (IP) theft poses serious concerns. In contrast, FPGA designers do not configure them with sensitive IPs unless the delivery of the product is completed [277]. Cybercriminals can replicate FPGA applications by intercepting their programming bitstream or reading the internal memory. Modern FPGAs have started using advanced encryption key standard (AES) with the battery-backed SRAM 256-bit or 384-bit security key, AES with the eFUSE key, on-chip bitstream keyed-Hash Message Authentication Code (HMAC) algorithm, bitstream authentication, etc., can mitigate the risks, protect intellectual property, and improve the overall safety of FPGA devices. FPGA-based edge devices exploit AI and ML capabilities for the processing of sensed data and subsequently reduce network bandwidth requirements and dependence on cloud processing. Also, vendors are providing IP cores like OpenVINO, Vitis-AI, etc., to leverage FPGA interfaces for the optimization and deployment of deep learning (DL) models [278]. Open Visual Inference and Neural Network Optimization (OpenVINO) is an open-source toolkit from Intel that facilitates quicker inference of deep learning models on hardware accelerators and easy heterogeneous execution across numerous hardware platforms.