###### Abstract  
With the advent of the Internet of Things (IoT) and cloud computing technologies, vast amounts of data are being created and communicated in IoT networks. Block ciphers are being used to protect these data from malicious attacks. Massive computation overheads introduced by bulk encryption using block ciphers can become a performance bottleneck of the server, requiring high throughput. As the need for high-speed encryption required for such communications has emerged, research is underway to utilize a graphics processor for encryption processing based on the high processing power of the GPU. Applying bit-slicing of lightweight ciphers was not covered in the previous implementation of lightweight ciphers on GPU architecture. In this paper, we implemented PRESENT and GIFT lightweight block ciphers GPU architectures. It minimizes the computation overhead caused by optimizing the algorithm by applying the bit-slicing technique. We performed practical analysis by testing practical use cases. We tested PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128 block ciphers in RTX3060 platforms. The throughput of the exhaustive search are 553.932 Gbps, 529.952 Gbps, 583.859 Gbps, and 214.284 Gbps for PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128, respectively. For the case of data encryption, it achieved 24.264 Gbps, 24.522 Gbps, 85.283 Gbps, and 10.723 Gbps for PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128, respectively. Specifically, the proposed implementation of a PRESENT block cipher is approximately 4\(\times\) higher performance than the latest work that implements PRESENT block cipher. Lastly, the proposed implementation of a GIFT block cipher on GPU is the first implementation for the server environment.  
parallel processing; PRESENT; GIFT; lightweight block cipher; GPU implementation +
Footnote †: journal: Applied  
on general software implementations of LED, Piccolo, and PRESENT on GPU architectures. This includes lookup table-based implementations that use explore various memory types to store the lookup tables. By studying parallel granularity and overlapping data transfer and processing, this work significantly improved the performance of the three selected block ciphers on various GPU platforms. [PERSON] et al. [13] implemented the core part of each cipher with a manual inline pseudo-assembly code (i.e., Parallel Thread eXecution; PTX) and optimized each target cipher by considering the algorithmic characteristics of each cipher. This is the first GPU optimization study for CHAM, with throughput improvements of 10.7% and 67% compared to the previous best results.  
Another interesting optimization technique is to bit-slicing the lightweight ciphers, which was covered in the previous studies. Bit-slicing was first used by [PERSON] [14] instead of lookup tables to speed up the software implementation of DES. Bit-slicing refers to a technique of bit-wise operation by collecting each bit in several blocks. In this way, multiple blocks can be processed in parallel. [PERSON] et al. [4] achieved the highest AES throughput on GPU using the bit-slicing technique. Moreover, some other related works that implement PRESENT [15], GIFT [16] and AES [17] on embedded devices (e.g., microcontroller) also showed that a bit-slicing based technique can achieve the best performance. However, in embedded software, a bit-slicing technique only processes two blocks [17] due to high register utilization. The advanced bit-slicing technique that processes a large number of blocks (i.e., larger than two blocks) is not widely explored as there are insufficient registers to hold the intermediate results. However, GPU devices do not have such limitations as there are plenty of registers available, which is suitable for this advanced bit-slicing technique. This motivates us to improve the throughput of PRESENT and GIFT lightweight ciphers using the advanced bit-slicing technique on the GPU.