{"ID":2881276,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13298","arxiv_id":"2508.13298","title":"Harnessing the Full Potential of RRAMs through Scalable and Distributed In-Memory Computing with Integrated Error Correction","abstract":"Exponential growth in global computing demand is exacerbated due to the higher-energy requirements of conventional architectures, primarily due to energy-intensive data movement. In-memory computing with Resistive Random Access Memory (RRAM) addresses this by co-integrating memory and processing, but faces significant hurdles related to device-level non-idealities and poor scalability for large computing tasks. Here, we introduce MELISO+ (In-Memory Linear Solver), a full-stack, distributed framework for energy-efficient in-memory computing. MELISO+ proposes a novel two-tier error correction mechanism to mitigate device non-idealities and develops a distributed RRAM computing framework to enable matrix computations exceeding dimensions of $65,000\\times65,000$. This approach reduces first- and second-order arithmetic errors due to device non-idealities by over $90\\%$, enhances energy efficiency by three to five orders of magnitude, and decreases latency 100-fold. Hence, MELISO+ allows lower-precision RRAM devices to outperform high-precision device alternatives in accuracy, energy and latency metrics. By unifying algorithm-hardware co-design with scalable architecture, MELISO+ significantly advances sustainable, high-dimensional computing suitable for applications like large language models and generative AI.","short_abstract":"Exponential growth in global computing demand is exacerbated due to the higher-energy requirements of conventional architectures, primarily due to energy-intensive data movement. In-memory computing with Resistive Random Access Memory (RRAM) addresses this by co-integrating memory and processing, but faces significant...","url_abs":"https://arxiv.org/abs/2508.13298","url_pdf":"https://arxiv.org/pdf/2508.13298v2","authors":"[\"Huynh Q. N. Vo\",\"Md Tawsif Rahman Chowdhury\",\"Paritosh Ramanan\",\"Murat Yildirim\",\"Gozde Tutuncuoglu\"]","published":"2025-08-18T18:29:05Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.AR\",\"cs.ET\",\"cs.PF\",\"eess.SY\"]","methods":"[\"Language Model\"]","has_code":false}