# Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence
## 2 Market and Trends of Memory Technologies
### General Context and Evolution of Nonvolatile Memories
#### 2.2.4 Development of New Computing Systems  
The previous paragraphs highlighted the (1) current data exponential increase, (2) energy increase, and (3) performance limitations of current computing systems. For these reasons, new computing systems are currently being developed and used. In particular, near memory or in-memory computing are proposed to solve the memory wall and the high energy and time cost of data transfer in data centers. However, there is a tradeoff among the existing systems between flexibility and efficiency [1], and choosing the appropriate configuration for each purpose is key.  
We can make the following classifications of computing systems depending on the application, in terms of increasing energy efficiency [38]:  
* CPU: they are fully programmable and versatile (can execute any function). There is a clear separation between logic and memory.
* GPU: there is a shared memory architecture with thousands of cores; they operate with high parallel workload.
* Near-memory processing: this system aims to supply high performance and high-density memories as close as possible to the processing units. They are dedicated to data-intensive computing (AI, graph processing, and optimization processing). They combine local and shared memory. They are used for cloud and edge devices.
* In-memory processing: in this system, computing and memory are collocated, which eliminates the von Neumann bottleneck. Major changes in both memory and computing units must be achieved in order to couple data processing and storage. In-memory processing is used for vector processing (AI with limited model size) and is mostly used for edge devices.
* Neuromorphic dynamical systems: they are brain inspired and merge logic and memory. They are used for AI or autonomous systems.  
Among all these systems, there are many artificial neural networks and especially deep learning networks that have matched or surpassed human level capabilities. These \"neuromorphic circuits\" are the subject of interest for the scientific community.  
Deep neural networks (DNNs), or large-scale layered networks of artificial neurons, have profoundly transformed the field of machine learning and represent the state-of-the-art in a variety of video, image, audio, and text processing tasks. They are already deployed in many real-world applications such as Google's image and voice search, Apple's Siri, Facebook's DeepText and DeepFace, Microsoft's Skype Translator, and many others [40]. They should also enter a wider range of applications in the future, including autonomous vehicles, education, and healthcare. The rise of neuromorphic circuits offers unique opportunities for emerging memories. Among them, two terminal back-end memories integrated in crossbar arrays could significantly improve the efficiency of deep neural networks as will be discussed in the next section.  
In conclusion, these new systems, offering significant computing energy, are also very demanding in terms of high reliability memories and require new memory technologies with excellent capacity, bandwidth, and performance. Research perspectives on emerging memories for new non von Neumann systems are discussed in the next sections.