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Abstract-At the basis of proteins identification we have a string matching algorithm, which has a computational complexity that scales with the length of both the searched and the reference string. This complexity, as well as the fact that to match a single protein we need multiple search of different string in the whole database, makes the protein identification a computational intensive task taking tens of seconds to complete. When performing this task with General Purpose Processors (GPPs), as it might be in a large scale installation (such as medical or research centers), this long execution time translates into a high energy requirement which greatly impacts the scalability and maintenance cost of the system. This paper illustrates a possible way to exploit Field Programmable Gate Arrays (FPGAs) to implement a string matching algorithm with an higher energy efficiency, up to 6 times better, than a standard GPP; such solution can be a building block for large-scale installations aimed at improving protein identification.

In the last few years Internet of Things (IoT) applications are moving from the cloud-sensor paradigm to a more variegated structure where IoT nodes interact with an intermediate fog computing layer. To enable compute-intensive tasks to be executed near the source of the data, fog computing nodes should provide enough performance and be sufficiently energy efficient to run on the field. Within this context, embedded Field Programmable Gate Array (FPGA) can be used to improve the performance per Watt ratio of fog computing nodes. In this paper we present Fog Acceleration through Reconigurable Devices (FARD), a distributed system that exploits FPGAs to accelerate compute-intensive tasks in fog computing applications. FARD is able to efficiently run distributed fog applications thanks to a well-defined application structure, a per-application isolated network overlay and thanks to the acceleration of tasks. Results show energy efficiency improvements while efficiently enabling cooperation across fog nodes.

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