AI Accelerators for Standalone Computer

Deep learning has become ubiquitous with many sensors and edge devices. In many situations, it needs to process data in real-time consistently. Recently, cloud-based AI systems reached the limit due to the bandwidth. Therefore, standalone computing, which transfers AI calculations to edge devices, is a solution to reduce the required traffic between data generator and processor. However, real-time deep learning inference at the edge has high computing power and memory demands. This recent AI system trend has rapidly raised the system’s power consumption. Many market leaders and researchers have designed integrated systems to reduce power consumption, including GPU-based acceleration APIs or specialized AI accelerators. In the beginning, Intel introduced a new type of coprocessor in 1989. Nvidia started building the CUDA ecosystem afterward. Finally, many IT companies introduce their own devices and APIs. These solutions enable high speed and performance, better security mechanisms, scalability, and better data management.