The field programmable gate array (FPGA) is used to build an artificial neural network in hardware. Architecture for a digital system is devised to execute a feed-forward multilayer neural network. ANN and CNN are very commonly used architectures. Verilog is utilized to describe the designed architecture. For the computation of certain tasks, a neural network’s distributed architecture structure makes it potentially efficient. The same features make neural nets suitable for application in VLSI technology. For the hardware of a neural network, a single neuron must be effectively implemented (NN). Reprogrammable computer systems based on FPGAs are useful for hardware implementations of neural networks.

An embedded time interval data acquisition system (DAS) is developed for zero power reactor (ZPR) noise experiments. The system is capable of measuring the correlation or probability distribution of a random process. The design is totally implemented on a single Field Programmable Gate Array (FPGA). The architecture is tested on different FPGA platforms with different speed grades and hardware resources. Generic experimental values for time resolution and inter-event dead time of the system are 2.22 ns and 6.67 ns respectively. The DAS can record around 48-bit x 790 kS/s utilizing its built-in fast memory. The system can measure very long time intervals due to its 48-bit timing structure design. As the architecture can work on a typical FPGA, this is a low cost experimental tool and needs little time to be established. In addition, revisions are easily possible through its reprogramming capability. The performance of the system is checked and verified experimentally.

This work present an efficient hardware architecture of Support Vector Machine (SVM) for the classification of Hyperspectral remotely sensed data using High Level Synthesis (HLS) method. The high classification time and power consumption in traditional classification of remotely sensed data is the main motivation for this work. Therefore presented work helps to classify the remotely sensed data in real-time and to take immediate action during the natural disaster. An embedded based SVM is designed and implemented on Zynq SoC for classification of hyperspectral images. The data set of remotely sensed data are tested on different platforms and the performance is compared with existing works. Novelty in our proposed work is extend the HLS based FPGA implantation to the onboard classification system in remote sensing. The experimental results for selected data set from different class shows that our architecture on Zynq 7000 implementation generates a delay of 11.26 μs and power consumption of 1.7 Watts, which is extremely better as compared to other Field Programmable Gate Array (FPGA) implementation using Hardware description Language (HDL) and Central Processing Unit (CPU) implementation.