# Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain
## 2 State-of-the-Art
### Multimedia Multiprocessor Embedded Architecture  
The multimedia application deployment on embedded systems has undergone a remarkable evolution, and different platforms have become adequate and efficient for the implementation of different applications. While some platforms have been considered for implementation and testing, others have enabled prototyping and the implementation of optimized architectures for multimedia applications.  
The existence of parallel applications has increased the importance of multi-core processors that resulted in the emergence of GPGPUs (General Purpose GPUs). The Graphics Processing Unit is a coprocessor, originally developed to accelerate graphics applications, and that consists of hundreds of cores and is capable of processing thousands of threads. The cores of a GPU execute the same instruction sequence but possibly on different data elements, thus turning the GPU into a predilection solution for a large variety of applications, particularly involving matrices, such as deep learning, machine learning, augmented reality, or virtual reality, to mention but a few. Yet, GPUs intrinsically come across with high energy consumption [20], thereby becoming unsuitable for a lightweight solution we are targeting in the present paper.  
Consequently, alternative solutions are searched for, and the FPGA family is a good candidate (note that server manufactures such as Intel have also preferred FPGAs for their lower energy consumption [21]).  
In fact, with the advent of heterogeneous computing systems such as the Xilinx Zynq UltraScale+ multiprocessor system-on-chip (MPSoC) [22], different processing units can be embedded in the System-on-Chip (SoC) to meet the growing requirements of the applications (performance/energy constraints). Moreover, hardware solutions offered by FPGA platforms provide the expected speed-up, while reducing the energy consumption. By connecting an FPGA-based accelerator to a programmable Central Processing Unit (CPU), performance and energy benefits are obtained while retaining more flexibility than an Application Specific Integrated Circuit (ASIC).  
For instance, in [23], the embedded experimentation of the Viola Jones face detection algorithm is based on a mixed HW/SW architecture; in this way, thanks to the processing parallelism, a system more stable than a classical PC is obtained. For an identical stability, the implementation of a massively parallel architecture on FPGA becomes faster.  
Compared to GPU, FPGA is certainly less efficient, but its energy consumption is lower. The use of a heterogeneous architecture based on parallel processors with a hardware IP was presented in [24]. The application is implemented on a multi-core Zynq platform using dual ARM processors. The face detection application provided a 7.8 times gain, compared to a classical dual ARM processor architecture.  
Raspberry Pi is a particular case of a multiprocessor ARM embedded platform that, despite its low computation and storage resources, continuously gains popularity thanks to its low cost and its use in education/academic programs. In [25], a new face detector adapted to on-board systems has been proposed. Based on the construction of face detection networks with low computational cost and sufficient capacity, this efficient method uses convolution factorization to build the network with scattered connections. The EagleEyeruns on the embedded device based on the ARM Cortex-A53 (Raspberry Pi1 3b+ [26; 27; 28; 29]) at 21 FPS with the input of VGA resolution with better functional accuracy than methods with the same order of computational complexity.  
This concise state-of-the-art brings to light that FPGA is considered today's mainstream embedded solutions for multimedia applications, and that Raspberry Pi is the challenger. Yet, the applicative borders between FPGA and Raspberry Pi are not clearly drawn. Our paper studies whether a lightweight implementation of a PoS Blockchain supporting a video fingerprinting application can be deployed on a Raspberry Pi 3 or not.