neuromorphic memory optimization

This project explores memory-efficient neuromorphic accelerator design by analyzing and optimizing synaptic weight storage and access patterns. It includes hardware–software co-design using Verilog and Python, weight quantization (8/16/32-bit), access tracing, visualization, and accuracy–memory trade-off evaluation for spiking neural networks.