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This paper presents an implementation of multilayer feed forward neural networks (NN) to optimize CMOS analog circuits. For modeling and design recently neural network computational modules have got acceptance as an unorthodox and useful tool. To achieve high performance of active or passive circuit component neural network can be trained accordingly. A well trained neural network can produce more accurate outcome depending on its learning capability. Neural network model can replace empirical modeling solutions limited by range and accuracy.[2] Neural network models are easy to obtain for new circuits or devices which can replace analytical methods. Numerical modeling methods can also be replaced by neural network model due to their computationally expansive behavior.[2][10][20]. The pro- posed implementation is aimed at reducing resource requirement, without much compromise on the speed. The NN ensures proper functioning by assigning the appropriate inputs, weights, biases, and excitation function of the layer that is currently being computed. The concept used is shown to be very effective in reducing resource requirements and enhancing speed.
The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplica
In this paper an attempt has been made to identify most important human resource factors and propose a diagnostic model based on the back-propagation and connectionist model approaches of artificial neural network (ANN). The focus of the study is on
The widespread application of artificial neural networks has prompted researchers to experiment with FPGA and customized ASIC designs to speed up their computation. These implementation efforts have generally focused on weight multiplication and sign
There are several indications that brain is organized not on a basis of individual unreliable neurons, but on a micro-circuital scale providing Lego blocks employed to create complex architectures. At such an intermediate scale, the firing activity i
Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures. These computing systems can be further improved upon by using emerging technologies that are more efficient than CMOS for neural computation.