What are the emerging memory technologies and architectures for VLSI systems?

As per my point of view, Resistive RAM is at the forefront of next-generation memory technologies, offering a range of benefits from energy efficiency to scalability and integration with AI and neuromorphic systems. While there are still hurdles to overcome before it becomes mainstream, the potential of ReRAM to reshape industries and drive future technological advancements is undeniable. It represents a pivotal step towards the creation of faster, smarter, and more efficient computing systems that could transform everything from consumer electronics to advanced scientific research.

I think ReRAM (Resistive RAM) is one of the promising non-volatile memory, offering fast speeds, high endurance, and low power, making it a candidate for future computing. However, there are many key design challenges including managing the variability of resistive switching materials and ensuring consistent performance at smaller scales. Integrating ReRAM with existing CMOS technology seems difficult, particularly in maintaining the thermal and electrical stability needed for reliable operation. Overcoming these challenges is crucial for ReRAM and other emerging memories to scale and achieve mainstream adoption in AI, neuromorphic computing, and high-performance storage.

Emerging memory technologies for VLSI systems focus on improving performance, power efficiency, and scalability. Non-volatile memories (NVMs) such as Resistive RAM (ReRAM), Magnetoresistive RAM (MRAM), and Phase-Change Memory (PCM) are gaining traction due to their ability to retain data without power, offering advantages in speed and endurance. Architectures like 3D stacking and monolithic 3D integration enable higher memory density by layering circuits vertically, optimizing space and performance. Hybrid memory architectures combine volatile (e.g., DRAM) and non-volatile components to balance speed and energy efficiency. In-memory computing is another emerging trend, aiming to reduce latency by processing data directly within memory.

I think STT-RAM (Spin-Transfer Torque RAM) and SOT-RAM (Spin-Orbit Torque RAM) are promising non-volatile memories, offering fast switching speeds, high endurance, and low power consumption. STT-RAM uses the spin of electrons to change the magnetic state of a material, which is energy-efficient but faces challenges like high write currents and potential reliability issues at smaller nodes. SOT-RAM, based on spin-orbit interactions, offers even faster switching with lower write currents than STT-RAM, but its complex fabrication process and integration with existing technology remain challenging. The future of STT and SOT-RAM hinges on reducing write currents and enhancing scalability for next-gen computing systems.

Near/In-Memory Computing reduces data transfer between memory and processors, significantly lowering latency and energy usage—making it highly effective for AI applications. 1. Industry leaders like Samsung, SK Hynix, TSMC, Intel, IBM are extensively working on IMC prototypes. 2. Startups such as Mythic and UPmem are innovating in this field; Mythic focuses on analog compute-in-memory solutions for low-power AI, while UPmem integrates processors directly within DRAM for enhanced data processing speeds. 3. Integrating logic into memory without compromising performance remains a challenge, but emerging technologies like 3D-stacked memory and ReRAM offer promising solutions. Research on SRAM and DRAM based IMC has shown a lot of potential.

HMC uses a 3D architecture, combining stacked DRAM and a logic layer to achieve much higher bandwidth than traditional DRAM architectures. In a high-performance computing project, I utilised HMC technology to accelerate data-intensive tasks like machine learning and real-time analytics. The ability of HMC to handle massive amounts of data at high speeds made it an ideal solution for the computational demands of advanced VLSI systems. The efficiency of the HMC design is particularly relevant for applications requiring fast data access and processing, such as AI and big data analytics.