## 1 Introduction  
A large number of modern cryptographic protocols are based on physical unclonable functions (PUF) implementations, which are used for key agreement and device authentication [1, 2, 3, 4, 5]. Memory-based PUFs are popular among other implemented PUFs due to being a major component in many electronic devices and requiring minimum (or no) additional circuit for PUF operation [6, 7]. Dynamic random access memory (DRAM)-based PUFs provide large address space and utilize several controllable properties to generate unique identifiers for identification and authentication purposes [8, 9, 10, 11]. Recently, researchers have proposed DRAM latency-based PUFs, which can be used to provide random device signatures by exploiting the timing parameters (e.g., activation (t\({}_{\text{RCD}}\)), precharge time (t\({}_{\text{RP}}\)), etc. [12, 13]). Reliability and robustness are two fundamental properties of a desirable PUF, which prove the independence of output responses on internal/external noises and ambient conditions. Most of the existing PUFs use some post-processing techniques, which require helper data algorithms and complex error correction codes (ECCs) to extract reliable responses and conduct a proper authentication procedure [8, 14]. However, these methods cause significant hardware/computational overheads and additional Non-volatile memory (NVM) to store helper data in addition to their security defects [15, 16, 17]. Most of the proposed DRAM PUFs employ different pre-selection mechanisms to eliminate dependent or unstable cells and decrease the ECC overheads [12; 13]. Pre-selection mechanisms consist of running multiple tests on the PUF and selecting the qualified cells using selection algorithms. These approaches limit the challenge-response pairs (CRP) space and entail additional runtime and costs. In such a way, resource-constrained nodes in the internet of things (IoT) applications cannot benefit from a DRAM PUF-based authentication. In [18], [PERSON] et al. proposed a DRAM-based authentication scheme without the need for PUFs in which a deep convolutional neural network (CNN) is utilized to authenticate DRAM Integrated Circuits (ICs) using raw power-up values. This method is secure against machine learning attacks and eliminates error correction mechanisms. However, this scheme can result in increased runtime because of requiring power cycles to extract power-up characteristics. Using CNN to extract unique features of DRAM at the device side can cause extra overhead for resource-constrained nodes. Most of the existing PUF-based authentication methods for IoT applications exploit strong PUFs, such as delay-based PUFs, which require dedicated circuits. Additionally, most of these methods rely on complex error correction algorithms to extract reliable responses, leading to add extra overheads to the device [19; 20]. Using DRAMs as the major components of many devices can eliminate the need for dedicated PUF circuits. Some latency-based DRAM PUFs, which do not need power cycles and waiting periods, can be evaluated as appropriate approaches to configure a lightweight authentication [12; 13; 18; 21; 22]. In this paper, we propose deep PUF that utilizes all latency failures in different DRAM blocks to expand the CRP space and organize a robust authentication without employing any pre-selection algorithms or post-correction mechanisms. We also move the entire burden of CNN implementation and device identification to the server-side to overcome the challenges of resource-constrained devices. The major contributions of this paper can be summarized as follows:  
* We propose deep PUF as a two-stage mechanism, including multi-label classification and challenge verification, to provide a robust and lightweight device authentication without error-correcting codes and other pre-filtering methods. * We implement two types of latency-based proposals (\(\text{t}_{\text{RCD}}\) and \(\text{t}_{\text{RP}}\) PUFs) as the fast runtime accessible DRAM PUFs and analyze their characteristics to train the CNN.