Aug 17, 2022 · Compute-in-memory (CIM) based on resistive random-access memory (RRAM) 1 promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM...

RRAM-enabled accelerators can solve the von Neumann bottleneck and meet the ever-growing computing needs of applications such as Artificial Intelligence (AI). In this paper, we discuss progress and challenges in RRAM-based accelerators for AI …

NeuRRAM is the first fully integrated (including all essential modules for end-to-end neural network support) and large-scale (48 cores, 3 million synapses, and 12 thousand neurons) demonstration of a complete RRAM-CIM hardware capable of performing diverse AI tasks.

Aug 25, 2023 · We propose to exploit the advantages of the RRAM devices combined with the flexibility of RISC-V cores by integrating multiple RRAM-based blocks into a RISC-V core via Memory Mapped I/O (MMIO), resulting in an architecture which can be reconfigured in software.

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Nov 6, 2023 · In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME.

Resistive Memory (RRAM) based computing-in-memory (CIM) can play a key role in intermittently operated AI edge devices and sensors [1] – [3]. By minimizing the d.

Sep 22, 2022 · The use of compute-in-memory (CIM) hardware using resistive random-access memory (RRAM) devices is expected to improve the energy efficiency of artificial intelligence systems by...

RRAM technology is a promising candidate for implementing efficient AI accelerators with extensive multiply-accumulate operations. By scaling RRAM devices to th.

Mar 18, 2025 · Explore how RERAM technology enhances AI hardware performance in neuromorphic computing applications. The RRAM-based neuromorphic computing system (NCS) has garnered significant attention due to its superior data processing capabilities and energy efficiency compared to traditional architectures.