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…
The Global Market for Big Data Technologies and Services is Projected to Reach $60 Billion by 2022
GIA launches comprehensive analysis of industry segments, trends, growth drivers, market share, size and demand forecasts on the global Big Data market. The global market for Big Data Technologies and Services is projected to reach $60 billion by 2022 , driven by soaring digital data volumes in organizations and the resulting need to turn this data into valuable insights for enhancing operational efficiency, tapping new opportunities and gaining competitive edge. Defined as a natural result of mankind’s obsession with information technology and digitalization, “Big Data” refers to extremely large sets of structured, semi-structured and unstructured data of different types, including text, audio or video, generated from diverse data sources that has the potential to be mined for required information. While data supply associated with big data ecosystem has always been large and voluminous in most organizations, the ability to use these large datasets and convert them into meaningf
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