Asynchronous NOCs are most prominent in present SOC designs, due to their low dynamic power consumption, modularity, heterogeneous nature, and robustness to the process variations. Though asynchronous designs are proved efficient over synchronous counterparts, they have some severe drawbacks when area and speed are considered, due to complex handshake control circuits which increase the static power loss. Quasidelay insensitive (QDI) class of asynchronous NOCs based on 2-phase encoding is proved beneficial for speed and throughput enhancement but with complex design. The work has introduced lightweight minimal buffer router based on LEDR encoding to design a low power, high speed with compact NOC architecture. Then, minimal buffer router with FSM-based arbiter and priority assigner block is designed to enhance the speed, power, and area. This proposed work achieves zero dynamic power consumption with a total power consumption of less than 0.082 W with a router latency of 0.8 ns.
There are numerous image classification strategies are developed in deep learning. However, due to the complexity of images, conventional image classification strategies have been incapable to meet real application needs. As the amount of pixel information rises, the classification becomes more difficult. However, CNN is widely used method for object identification in picture due to its simple and accurate, but still, it remains hazy which strategies are most supportive for analysing and distinguishing the objects in pictures. In this paper we introduced a CNN network and clustering-based technique called IBCNN to perform classification based on patch extraction. The proposed method can accomplish their goals in the following four different ways: a) Automatic Kernel selection; b) resilient patch size selection; c) CNN layer; and d) pooling layer modification. In addition, it also modifies the pooling layer with average value and calculate the pixel size. The proposed method was applied on ten different image datasets. Finally, the proposed model is compared to three benchmarking models: such as WCNN, MLP, and ELM-CNN to estimate its performance. The obtained results shows that the proposed method gives competitive results compared to the other models.
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