In-memory calculation for ML applications


Conventional computing architecture fails to cope with the enormous cognitive burden posed by machine learning (ML) and artificial intelligence (AI) algorithms such as deep neural networks (DNN) and computer networks. convolutional neurons (CNN). Non-Von Neumann compute architectures such as in-memory computing (IMC) / in-memory processing (PIM) are widely studied with the aim of acquiring hardware solutions to meet the low and high computational needs of these techniques. Interested in machine learning, learn from this machine learning course in London to gain real-time understanding and experience.

What is in-memory computing?

Data is stored in RAM instead of databases on disks via in-memory computing (IMC). Since the information stored in RAM was available instantly, but the data stored on the drives is restricted by networking and the disk accelerates, this reduces the I / O transaction and ACID requirements of the users. OLTP systems and significantly speeds up data availability. IMC can cache large amounts of data, enabling lightning-fast reaction times, as well as data file storage, which can help achieve maximum potential.

Here are the advantages of in-memory computing.

  • In-memory offers high speed technology
  • Scalability is best with in-memory computing
  • We can get real-time information using this technology
  • In-Memory offers many use cases that benefit our everyday applications
  • It also allows the integration of technology.

Why do we need In-Memory?

Businesses must find a way to cope with the continual increase in accessible information and endless expectations of faster and more efficient efficiency to maintain a strategic advantage and meet today’s needs for the highest quality. on duty. In-memory computer systems are gaining ground because of this. Since then, In Computing has been concerned with the amount of information that can be consumed and processed in a short period of time. Conventional methods, which are typically based on hard disks and database systems using SQL database languages, are insufficient for modern business intelligence (BI) demands, which include lightning-fast processing and real-time data scalability.

Benefits of using in-memory computing in machine learning

Now let’s take a look at some advantages of in-memory computing in machine learning. Find out more about ML. check out this blog What is Machine Learning.

  • In-memory computing in machine learning is ideal for collecting data from many inputs and providing access as quickly as possible.
  • Machine learning applications can quickly retrieve information from a multitude of inputs by using many units for RAM.
  • Anytime there is a single large data stream that can take a long time to analyze and sift through in depth, this technology can help.
  • Machine learning has benefited from in-memory computing because it provides quick conclusions, which can be essential in business operations.
  • As a result, the revolution in faster data processing could lead to increased revenue and profit savings.
  • Expenses incurred to develop in-memory computing will pay off in the long run.

The impact of in-memory processing on machine learning

Real-time data analysis is the fundamental and arguably most important result of in-memory computing. Information would not need to be transferred elsewhere for analysis due to multi-core computing and in-memory cache. Contemporary SSDs can reach speeds of up to 2.5 Gbps, but they fade all the same, unlike the incredible 134 Gbps that contemporary DRAM can achieve.

When it comes to accelerating a deep learning approach, fast and real processing of data is essential. When it comes to tasks such as sound synthesis, face detection, and image categorization, several types of neural networks (ANNs) have shown a substantial increase in quality. An ANN balances the load between nerve cells in response to new information in a procedure called training.

Developing a neural network takes a lot of time and computational power, and it all depends on your accuracy, how much data you have, and the type of neural network you train. Medium to extremely massive artificial neural networks can take days or even months to fully develop. The requirement to transmit mass information back and forth between memory space, which, as you may recall, is the slowest step in typical data analysis, is often blamed for the slow performance. Modern in-memory procedures have the ability to dramatically speed up network recycling by reducing ETL load.


The bottom line is that in-memory technology is starting to unleash a wave of creativity that is not based on Big Data which is becoming more and more accessible. It tears apart the costly mechanisms of traditional software, which has not kept up with the increase in data or the volume of needs. Refrigerators, thermometers, fluorescent lights, propulsion systems, and sometimes even heart monitors create flows of information that will not only educate us, and thus protect us, keep us better and allow each other to lead better fashions. life as the Internet extends from direct link to connectivity. We will begin to appreciate the facilities and pleasures that were previously only available in science books. In-memory computing is the feature that makes this revolution happen now.

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