The explosion in data generation has fueled the demand for improved memory and storage types at the workplace. The traditional memory systems have been struggling to keep up with the increased volume of data, the bandwidth requirements in the systems, and the speed requisite in the current generation systems. The emerging Big Data and artificial intelligence (AI) applications, including machine learning, drive innovations across many industries. The issues in making advancements in memory technologies to meet the evolving computing requirements present several challenges for the AI industry. Next-generation memory has fit in as the missing part of the puzzle. For instance, the existing tech DRAM works fast but consumes a lot of power, while both NAND and hard drives are cheap but work slow. The new memory types combine the speed of SRAM and the non- volatility of flash drives with almost unlimited endurance. Although the research on these technologies has been going on for a long time, there was a delay in reaching the commercial market. A significant proportion of the total power is spent on memory systems, as most of the area of current systems-on-chips (SOC) is occupied by memories. Moreover, the processing elements need to be fed with instructions and data from memories where memory plays a vital role in the system's performance.

1. INTRODUCTION

2. RESEARCH METHODOLOGY

3. EXECUTIVE SUMMARY

4. MARKET INSIGHTS

5. MARKET DYNAMICS

6. MARKET SEGMENTATION

7. KEY VENDOR PROFILES

8. INVESTMENT ANALYSIS

9. FUTURE OF THE MARKET