Core Concept - The article discusses the concept of "Compute In Memory" (CIM), which integrates storage and computation to enhance data processing efficiency and reduce energy consumption [1][20]. Group 1: Background and Need for CIM - Traditional computing architecture, known as the von Neumann architecture, separates storage and computation, leading to inefficiencies as data transfer speeds cannot keep up with processing speeds [2][10]. - The explosion of data in the internet era and the rise of AI have highlighted the limitations of this architecture, resulting in the emergence of the "memory wall" and "power wall" challenges [11][12]. - The "memory wall" refers to the inadequate data transfer speeds between storage and processors, while the "power wall" indicates high energy consumption during data transfer [13][16]. Group 2: Development of CIM - Research on CIM dates back to 1969, but significant advancements have only occurred in the 21st century due to improvements in chip and semiconductor technologies [23][26]. - Notable developments include the use of memristors for logic functions and the construction of CIM architectures for deep learning, which can achieve significant reductions in power consumption and increases in speed [27][28]. - The recent surge in AI demands has accelerated the development of CIM technologies, with numerous startups entering the field alongside established chip manufacturers [30][31]. Group 3: Technical Classification of CIM - CIM is categorized into three types based on the proximity of storage and computation: Processing Near Memory (PNM), Processing In Memory (PIM), and Computing In Memory (CIM) [34][35]. - PNM involves integrating storage and computation units to enhance data transfer efficiency, while PIM integrates computation capabilities directly into memory chips [36][40]. - CIM represents the true integration of storage and computation, eliminating the distinction between the two and allowing for efficient data processing directly within storage units [43][46]. Group 4: Applications of CIM - CIM is particularly suited for AI-related computations, including natural language processing and intelligent decision-making, where efficiency and energy consumption are critical [61][62]. - It also has potential applications in AIoT products and high-performance cloud computing scenarios, where traditional architectures struggle to meet diverse computational needs [63][66]. Group 5: Market Potential and Challenges - The global CIM technology market is projected to reach $30.63 billion by 2029, with a compound annual growth rate (CAGR) of 154.7% [79]. - Despite its potential, CIM faces technical challenges related to semiconductor processes and the establishment of a supportive ecosystem for design and testing tools [70][72]. - Market challenges include competition with traditional architectures and the need for cost-effective solutions that meet user demands [74][76].

Core Viewpoint - The article discusses the rapid advancements in AI and the challenges posed by the "memory wall" problem, highlighting the need for innovative storage solutions to meet the increasing demands of AI models and high-performance computing [1][2]. Group 1: Memory Wall and HBM Technology - The growth of AI models has led to an exponential increase in model parameters, creating significant demands on computing resources, particularly storage bandwidth [1]. - Traditional DRAM bandwidth growth is lagging behind processor performance, with DRAM bandwidth increasing only 1.6 times every two years compared to processor performance increasing threefold [1]. - HBM technology has emerged as a revolutionary solution, offering data transfer speeds of 1.2TB per second, significantly alleviating memory bandwidth pressure [2]. Group 2: 3D Ferroelectric RAM - 3D Ferroelectric RAM (FeRAM) is highlighted as a potential disruptor in the DRAM landscape, with companies like SunRise Memory developing innovative FeFET storage units that promise tenfold storage density improvements over traditional DRAM [4][5]. - This new technology boasts a 90% reduction in power consumption compared to traditional DRAM, making it particularly advantageous for energy-sensitive AI applications [5]. - SunRise Memory aims to leverage existing 3D NAND fabrication processes for mass production, indicating a strategic approach to commercialization [5][6]. Group 3: Other Emerging Storage Technologies - Neumonda GmbH and Ferroelectric Memory Co. are collaborating to develop "DRAM+" non-volatile memory, which integrates ferroelectric effects to create low-power, high-performance storage solutions [8][9]. - Imec's 2T0C DRAM architecture represents a significant innovation, allowing for higher density and improved performance by eliminating the need for capacitors [10][11]. - Phase Change Memory (PCM) is also gaining traction, with advancements in nanowire technology reducing power consumption significantly while maintaining high performance [19][20]. Group 4: Market Outlook and Industry Implications - The semiconductor industry is undergoing a transformation driven by AI, with various new storage technologies vying to replace traditional DRAM [25]. - The emergence of diverse storage solutions, including 3D Ferroelectric RAM, DRAM+, and IGZO 2T0C, indicates a shift towards a more versatile storage market that can cater to different application needs [25]. - The ongoing developments in storage technology are expected to reshape the semiconductor landscape, presenting both opportunities and challenges for industry players [25].