Compressor based Approximate Multipliers for Neural Network Accelerators

The objective of "Compressor Based Approximate Multipliers for Neural Network Accelerators" is to design and develop efficient approximate multipliers using compressor-based architectures to optimize the performance of neural network accelerators. The focus is on reducing power consumption, hardware complexity, and computation latency while maintaining acceptable accuracy, thereby enhancing the overall efficiency of machine learning applications in edge and embedded systems.