Multiply Accumulate

Multiply-accumulate (MAC) operations, fundamental to many computational tasks, are a central focus of research aiming to improve efficiency and performance in various applications, particularly deep learning. Current efforts concentrate on optimizing MAC unit design through novel architectures (e.g., convolutional neural networks, vision transformers) and algorithms (e.g., reinforcement learning, quantization techniques), often targeting reduced precision and hardware acceleration for improved energy efficiency. These advancements are crucial for enabling the deployment of computationally intensive models on resource-constrained devices and accelerating scientific computing across diverse fields.