A large number and a variety of sensors and actuators, also known as edge devices of the Internet of Things, belonging to various industries - health care monitoring, home automation, industrial automation, have become prevalent in today's world. These edge devices need to communicate data collected to the central system occasionally and often in burst mode which is then used for monitoring and control purposes. To ensure secure connections, Asymmetric or Public Key Cryptography (PKC) schemes are used in combination with Symmetric Cryptography schemes. RSA (Rivest - Shamir- Adleman) is one of the most prevalent public key cryptosystems, and has computationally intensive operations which might have a high latency when implemented in resource constrained environments. The objective of this thesis is to design an accelerator capable of increasing the speed of execution of the RSA algorithm in such resource constrained environments. The bottleneck of the algorithm is determined by analyzing the performance of the algorithm in various platforms - Intel Linux Machine, Raspberry Pi, Nios soft core processor. In designing the accelerator to speedup bottleneck function, we realize that the accelerator architecture will need to be changed according to the resources available to the accelerator. We use high level synthesis tools to explore the design space of the accelerator by taking into consideration system level aspects like the number of ports available to transfer inputs to the accelerator, the word size of the processor, etc. We also propose a new accelerator architecture for the bottleneck function and the algorithm it implements and compare the area and latency requirements of it with other designs obtained from design space exploration. The functionality of the design proposed is verified and prototyped in Zynq SoC of Xilinx Zedboard.
Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-1917 |
Date | 10 April 2020 |
Creators | Ramesh, Pavithra |
Publisher | ScholarWorks@UMass Amherst |
Source Sets | University of Massachusetts, Amherst |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Masters Theses |
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