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ML AI memory machine learning, artificial intelligence

Memory inflates the value of Artificial Intelligence and Machine Learning

The choice of memory plays a vital role in every successful AI application


Memory in technology is different from human memory. There is no forgetting or missing a part when it comes to memory in the tech sector as it remains as one of the most critical applications for enabling advances in Artificial Intelligence (AI) and Machine Learning (ML) processing.

The world has seen unprecedented changes since the invention of computers. The acceleration of development started quickly from the 1990s with computers slowly entering everyone’s daily life. Then came gaming, mobile, and cloud computing. A whole new line of inventions derailed from the small sprout in the past 30 years.

When Personal computers made a revolution, people began to look for more features in technology. That is when applications like Word, Excel, PowerPoint came to the center stage to complement the developing technology. However, the key role behind all these applications was played by memory. Memory accelerated in creating innovative technologies with the growing amount of data in its reserves.

The performance was pushed further and higher with the existence of graphical user interfaces, the web, and gaming portals. A memory type named Graphics DDR, designed to meet increased bandwidth demands was used in the protocol.


Role of memory in artificial intelligence and machine learning

Memory accelerated the performance of artificial intelligence and machine learning through its capacity and power efficiency. AI is a wide-ranging branch of computer concerned with building smart machines capable of performing tasks typically requires human intelligence. Whereas, machine learning is defined as an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being programmed. According to a report published by OpenAI, artificial intelligence and machine learning training capability has increased by a factor of 300,000 between 2012 and 2019. The report suggests that the growth was doubled every 3.43 months. There is a huge expansion in the use of AI and MI models, and training sets. In Silicon Valley improvement, a major part is taken by memory systems.


Memory in AI and ML systems consists of two vital tasks. They are,

  • Training– The process of teaching to the neural network requires large datasets to be presented to the neural network so that it can comprehend its task. It sometimes takes a time span of days or weeks to become proficient.
  • Interference– A trained neural data that it has never seen before is used with interference accuracy determined by how well the neural network was trained.

Cloud computing too helps in training neural network models. It splits the neural code among multiple servers, each running in parallel to improve training time and handle extremely large training sets. The priority of the memory runs in the ‘time-to-market’ incentive with the value created through neural models. However, AI models require higher memory bandwidth. The process takes place in the data center which accelerates the need for power-efficient and compact solutions to reduce the cost and improve ease of adoption. Once an AI/ML model is successfully trained, it can be deployed either in the data center or increasingly in IoT devices.


There are three types of memory from which AI and ML models choose depending on their needs.

On-chip memory: On-chip memory is the best choice for systems with small neural networks that only do deferring. This kind of memory is the fastest memory available. It has the best power efficiency, but by comparison, is severely limited in capacity.

High Bandwidth Memory (HBM): HBM was introduced in 2013 with high performance and 3D-stacked DRAM architecture. It provides high bandwidth and capacity in a very small footprint. By keeping the data rates low and the memory closer to the processor, HBM increases the overall system power efficiency. The newest of HBM memory is HBM2E, which is more complex and costly at the same time.

GDDR: The graphic double data rate (GDDR) memory started evolving two decades ago. It is originally designed for the gaming and graphics market. The current GDDR6 which is used in the generation supports data rates of up to 16 Gbps, allows a single DRAM device to achieve 64 GB/s of bandwidth.

The facilitation of Artificial Intelligence (AI) and Machine Learning (ML) models through memory datasets show no sign of slowing down in the demand for more computing performance. However, if the improvement of memory in the past 5 years replays at the same speed in the fore coming times, the tech world could see several models breaking the industry with further wide bandwidth and features.