Ever since the conception of the ideology known as the Internet of Things (IoT), our world is slowly approaching the brink of mankind's next technological revolution. The realization of IoT requires an enormous amount of sensor nodes to acquire inputs from the connected objects. Due to the lightweight nature of these sensors, constraints emerge in the form of limited power supply and area for the implementation of information security mechanism. To ensure security in the data transmitted by these sensors, lightweight cryptographic solutions are required. In this work, our goal is to implement a compact PRESENT cipher onto a Field Programmable Gate Array (FPGA) platform. Our proposed design uses an 8-bit datapath to reduce hardware size. Instead of a traditional look-up table (LUT) based S-Box, we have implemented a Boolean S-Box through Karnaugh mapping. Further factorization is also done to reduce the size of the Boolean S-Box. As a result, we have achieved the smallest FPGA implementation of the PRESENT cipher to date, requiring only 62 slices on the Virtex-5 XC5VLX50 platform. Our design also features a respectable throughput of 51.32 Mbps at the maximum frequency of 236.574 MHz.
Abstract:A new Stochastic Computing (SC) circuit design paradigm for image processing system is presented in this work. Two improved SC computational functions are derived, which are namely the stochastic scaled addition and stochastic absolute value of difference. Data correlation is also incorporated in the design for effective circuit size reduction without imposing accuracy degradation in the hardware implementations. The proposed SC functions are next employed to design the new and lightweight Sobel edge detection. Experimental results obtained from detailed test analysis have proven that new implementation has satisfactory accuracy level and higher fault tolerance capability in comparison with their conventional counterparts. The works proposed are also implemented on an Altera Cyclone V 5CGXFC7D6F31C6 FPGA for hardware complexity evaluation.
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