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CEA-Leti Scientists Present In-Memory Computing Pathways for Edge-AI & Neural Networks with 3D Architectures & Resistive-RAM

Papers at IEDM 2020 Explore Ways to Leverage 3D Technology's Strengths For Lowering Device Energy Consumption and Energy Lost in Data Transmission

www.leti-cea.com, Dec. 18, 2020 – 

GRENOBLE, France – Dec. 18, 2020 – CEA-Leti presented two papers this week at IEDM 2020 that confirm the advantages of combining 3D architectures and resistive-random-access-memories (RRAM) for in-memory computing (IMC), and their applications for Edge-AI and neural networks. The projects were designed to look at different ways to leverage 3D's strengths in lowering device energy consumption as well as energy lost during data transfer.

Today, storage-class memories like high-density 3D crossbar RRAM are promising for applications requiring a large amount of on-chip memory, explained the paper 3D RRAMs with Gate-All-Around (GAA) Stacked Nanosheet Transistors for In-Memory-Computing. RRAM is a leading candidate due to its high density, good scalability, low operating voltage, and easy integration with CMOS devices. Another attractive aspect of RRAM is their ability to perform primitive Boolean logic operations for in-memory and neuromorphic computing. However, if the 1T1R design is the most reliable architecture for IMC, the cell size remains limited by the conventional access transistor.

In that paper, scientists proposed a novel 3D memory-cube technology that targets non-volatility, high density, high parallelism of operations and co-integration with CMOS, which makes it very attractive for storage-class memories and IMC. The memory cube is based on a one-transistor / one-RRAM (1T1R) bitcell. 1T1R architecture benefits from the high density of vertically stacked nanosheet (NS) transistors, developed for advanced CMOS, which feature excellent 3D integration scalability.

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