The Global Market for Private Tutoring Services is Forecast to Reach US$227 Billion by 2022

The global Hybrid Memory Cube (HMC) and High-Bandwidth Memory (HBM) market is projected to reach US$4.7 billion by 2025, driven by the blistering pace of growth of AI-assisted technologies, increase in AI workloads and the ensuing need for more memory in AI servers. Making data readily available for AI initiatives is key for successful AI projects and this requires data to be stored closer to the processing tasks to speed up data processing and deliver business value by providing timely and actionable insights. On an average, AI servers require over 8 times the amount of DRAM capacity and over three times the amount of SSDs when compared to a traditional server.  This need for memory will only grow bigger and more urgent with the growth of deep learning, machine learning, expanding size of neural networks and emergence of newer and more complex neural networks such as Feedforward Neural Network, Radial basis function Neural Network, Kohonen Self Organizing Neural Network, Recurrent Neural Network (RNN), Convolutional Neural Network and Modular Neural Network. For instance, Machine Learning (ML) involves continuous running of algorithms against historical data, creating a hypothesis, analyzing new data in real-time as and how it is generated and fed through the IoT system. Similarly, in Deep Learning incoming processed data sets are used to train multi-layered neural networks to continuously learn to interpret data with greater speed and accuracy. To achieve all of these with efficiency and effectiveness algorithms need dynamic on-the-go access to cold (old historic data), warm (recently generated data) and hot (current sensor generated data).  Read More…