Single-Cell Universal Logic-in-Memory Using 2T-nC FeRAM: An Area and Energy-Efficient Approach for Bulk Bitwise Computation

2025-09-22

AI Summary

DRAM suffers from low energy efficiency due to periodic refresh operations. To address this, this work proposes a single-cell compute-in-memory architecture based on 2T-nC ferroelectric random-access memory (FeRAM). By introducing a quasi-non-destructive readout (QNRO) mechanism, it enables the MINORITY logic function natively within a single memory cell—supporting universal Boolean operations (e.g., NAND, NOR) without peripheral circuit reconfiguration. The design inherently combines non-volatility, ultra-low power consumption, and high density, while remaining compatible with 3D stacking and monolithic integration. SPICE simulations and experimental validation demonstrate that, across eight data-intensive workloads, the architecture achieves 2× higher performance and 2.5× better energy efficiency compared to conventional DRAM, alongside robust thermal stability. This work establishes a scalable device-architecture co-design paradigm for high-density, energy-efficient compute-in-memory systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This work presents a novel approach to configure 2T-nC ferroelectric RAM (FeRAM) for performing single cell logic-in-memory operations, highlighting its advantages in energy-efficient computation over conventional DRAM-based approaches. Unlike conventional 1T-1C dynamic RAM (DRAM), which incurs refresh overhead, 2T-nC FeRAM offers a promising alternative as a non-volatile memory solution with low energy consumption. Our key findings include the potential of quasi-nondestructive readout (QNRO) sensing in 2T-nC FeRAM for logic-in-memory (LiM) applications, demonstrating its inherent capability to perform inverting logic without requiring external modifications, a feature absent in traditional 1T-1C DRAM. We successfully implement the MINORITY function within a single cell of 2T-nC FeRAM, enabling universal NAND and NOR logic, validated through SPICE simulations and experimental data. Additionally, the research investigates the feasibility of 3D integration with 2T-nC FeRAM, showing substantial improvements in storage and computational density, facilitating bulk-bitwise computation. Our evaluation of eight real-world, data-intensive applications reveals that 2T-nC FeRAM achieves 2x higher performance and 2.5x lower energy consumption compared to DRAM. Furthermore, the thermal stability of stacked 2T-nC FeRAM is validated, confirming its reliable operation when integrated on a compute die. These findings emphasize the advantages of 2T-nC FeRAM for LiM, offering superior performance and energy efficiency over conventional DRAM.

Problem

• Designing energy-efficient logic-in-memory using 2T-nC FeRAM
• Overcoming refresh overhead of conventional 1T-1C DRAM
• Enabling universal logic and bulk bitwise computation

Innovation

• 2T-nC FeRAM enables single-cell logic-in-memory operations
• Quasi-nondestructive readout performs inverting logic inherently
• 3D integration boosts storage and computational density