Ultra low power of artificial cognitive memory for brain-like computation

The development of conventional computer is limited by the separated CPU and memory, as well as the desperate Moore's law. It's a trend to develop a brain-like computer architecture in order to solve above problems, where a new memory is highly needed. Artificial cognitive memory (ACM) based on neuromorphic circuits, integrates computation tightly with storage like the brain and is the key component to support brain-like computation. The power issue of ACM is critical, especially for the application of portable devices. In this paper, we systematically analyze the key factors of the energy consumption for building an ACM chip, such as element model, silicon technology, synaptic material, array selecting, weight modification method, routing strategy and the integration approaches. These results provide an in-depth insight for creating an ultra-low power brain-like computing platform.