Abstract:
Cloud-based systems have become very popular these days, which consists of a server and
node devices. These node devices are called edge devices or edge of the system. With ever
increasing size of data and complex algorithms, the load on servers have increased many folds.
Hence there is a requirement of shifting this load to the edge devices. Shifting load from server
to edge nodes is called edge computing. Now Fuzzy C-Means (FCM) is an unsupervised
machine learning algorithm that is used for data clustering. It is also called soft k-means or soft
clustering as each data point can belong to more than one cluster. In cloud-based applications
it can affect the performance of server due to its complexity. In edge computing systems
workload is put close to the edge where data is being created, this helps improve response time
and save bandwidth. FCM can be incorporated in various smart nodes-based devices by
assigning data to relative clusters. Image segmentation is one of such applications that can be
implemented using FCM. In proposed design FCM is implemented using multicore
architecture, which consists of P processing units called Tiles. Each Tile process a chunk of
data in parallel and final results are calculated by sharing of data between the Tiles. This
improves the overall performance of the system by speed up as compared to the traditional
sequential architecture. The proposed architecture can be used to design a smart edge
computing-based system with high performance and better throughput.
