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Recent research has demonstrated that shallow feedforward networks can learn non-trivial classification tasks with reduced computational complexity compared to deep learning architectures. This discovery suggests the potential for the development of unique hardware for fast and efficient shallow learning, while reducing energy consumption. The earliest artificial neural network, the Perceptron, was introduced approximately 65 years ago and consisted of just one layer. However, to address solutions for more complex classification tasks, more advanced neural network architectures consisting of numerous feedforward layers were later introduced. This is the essential component of the current implementation of deep learning algorithms.