Neuromorphic Computing For Real Time Handwritten Digit Recognition

The emergence of neuromorphic computing as a biologically inspired paradigm has created remarkable opportunities for ultra-low-latency, energy-efficient pattern recognition tasks. Conventional deep learning systems, while achieving state-of-the-art recognition accuracy, demand substantial computational resources and energy consumption that are unsuitable for edge and real-time deployment environments. This study presents a neuromorphic framework for real-time handwritten digit recognition leveraging Spiking Neural Networks (SNNs), which closely emulate the event-driven spike communication mechanism of biological neurons. The proposed architecture encodes pixel intensities from the MNIST benchmark dataset as temporal spike trains and processes them through a multi-layer SNN trained using the Spatio-Temporal Back-Propagation (STBP) algorithm with surrogate gradient approximation. Implemented using the snnTorch simulation framework integrated with PyTorch, the system achieves a test classification accuracy of 98.6% on the MNIST dataset while operating at an inference latency of less than 1 millisecond per sample. Energy consumption analysis demonstrates a reduction of approximately 75% compared to equivalent Artificial Neural Network (ANN) implementations. These results establish the proposed SNN architecture as a viable, resource-efficient alternative to conventional deep learning models for real-time handwriting recognition in embedded and neuromorphic hardware contexts.