PIM hardware accelerators for real-world problems

Artificial Intelligence (AI) neural networks have proven to be powerful tools for solving complex problems. However, their real-world applications face challenges, particularly in achieving real-time inference due to the high computational requirements for processing high-dimensional data. This issue can lead to accidents or inefficiencies in critical tasks like autonomous driving and medical devices. Consequently, reducing end-to-end latency has become a significant research focus. AI hardware accelerators with efficient calculation circuits have been developed to address this challenge and enhance performance. Processing in Memory (PIM) is a promising solution among these hardware architectures. PIM is a hardware structure that leverages in-memory computing to minimize power consumption and reduce latency bottlenecks. This study explores and introduces various PIM hardware accelerators that have been researched and commercialized in recent years. The goal is to provide an overview of cutting-edge solutions to reduce power consumption and latency in AI neural networks. By delving into these advancements, we aim to gain insights into how PIM can help overcome the real-time inference challenges in AI applications and improve the overall efficiency of neural network computations.