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Related papers: Stateful Logic In-Memory Using Gain-Cell eDRAM

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Processing-in-cache (PiC) and Processing-in-memory (PiM) architectures, especially those utilizing bit-line computing, offer promising solutions to mitigate data movement bottlenecks within the memory hierarchy. While previous studies have…

Computers and Society · Computer Science 2024-07-30 Dhruv Gajaria , Tosiron Adegbija , Kevin Gomez

In the era of artificial intelligence (AI), Transformer demonstrates its performance across various applications. The excessive amount of parameters incurs high latency and energy overhead when processed in the von Neumann architecture.…

Hardware Architecture · Computer Science 2025-02-14 Jae-Young Kim , Donghyuk Kim , Seungjae Yoo , Sungyeob Yoo , Teokkyu Suh , Joo-Young Kim

In most modern systems, the memory subsystem is managed and accessed at multiple different granularities at various resources. We observe that such multi-granularity management results in significant inefficiency in the memory subsystem.…

Hardware Architecture · Computer Science 2016-05-23 Vivek Seshadri

Knowledge graphs (KGs), with their structured representation capabilities, offer promising avenue for enhancing Retrieval Augmented Generation (RAG) systems, leading to the development of KG-RAG systems. Nevertheless, existing methods often…

Information Retrieval · Computer Science 2025-10-17 Yikuan Hu , Jifeng Zhu , Lanrui Tang , Chen Huang

Modern DRAM modules are often equipped with hardware error correction capabilities, especially for DRAM deployed in large-scale data centers, as process technology scaling has increased the susceptibility of these devices to errors. To…

Hardware Architecture · Computer Science 2017-06-29 Yixin Luo , Saugata Ghose , Tianshi Li , Sriram Govindan , Bikash Sharma , Bryan Kelly , Amirali Boroumand , Onur Mutlu

Running Large Language Models (LLMs) on edge devices is crucial for reducing latency, improving real-time processing, and enhancing privacy. By performing inference directly on the device, data does not need to be sent to the cloud,…

Hardware Architecture · Computer Science 2025-10-21 Tianhua Xia , Sai Qian Zhang

Retrieval-Augmented Generation (RAG) has shown significant improvements in various natural language processing tasks by integrating the strengths of large language models (LLMs) and external knowledge databases. However, RAG introduces long…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-04-26 Chao Jin , Zili Zhang , Xuanlin Jiang , Fangyue Liu , Xin Liu , Xuanzhe Liu , Xin Jin

We put forward a new proposal of designing charge-based logic devices considering a cyclic molecule that can be programmed and re-programmed for different functional logical operations and suitably engineered for data storage as well. The…

Mesoscale and Nanoscale Physics · Physics 2018-09-19 Moumita Patra , Santanu K. Maiti

Self-attention in Transformers generates dynamic operands that force conventional Compute-in-Memory (CIM) accelerators into costly non-volatile memory (NVM) reprogramming cycles, degrading throughput and stressing device endurance. Existing…

Hardware Architecture · Computer Science 2026-04-10 Md Zesun Ahmed Mia , Jiahui Duan , Kai Ni , Abhronil Sengupta

Recent advances in metamaterials and fabrication techniques have revived interest in mechanical computing. Contrary to techniques relying on static deformations of buckling beams or origami-based lattices, the integration of wave scattering…

Applied Physics · Physics 2025-11-04 Ethan Fort , Mohamed Mousa , Mostafa Nouh

Multi-agent reinforcement learning (MARL) algorithms have made promising progress in recent years by leveraging the centralized training and decentralized execution (CTDE) paradigm. However, existing MARL algorithms still suffer from the…

Machine Learning · Computer Science 2021-10-20 Xiao Ma , Wu-Jun Li

Today's high-performance architectures are increasingly constrained by data movement latency and energy overhead, as the slowdown of single-core performance scaling coincides with the rise of highly data-intensive workloads. In-memory…

Emerging Technologies · Computer Science 2026-05-06 Farzad Razi , Mehran Moghadam , Sercan Aygun , M. Hassan Najafi , Marc Riedel

Continuously estimating an agent's state space and a representation of its surroundings has proven vital towards full autonomy. A shared common ground among systems which successfully achieve this feat is the integration of previously…

Computer Vision and Pattern Recognition · Computer Science 2019-08-05 Gil Avraham , Yan Zuo , Thanuja Dharmasiri , Tom Drummond

Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by…

Hardware Architecture · Computer Science 2023-09-15 Onur Mutlu

Storage-class memory (SCM) combines the benefits of a solid-state memory, such as high-performance and robustness, with the archival capabilities and low cost of conventional hard-disk magnetic storage. Among candidate solid-state…

Hardware Architecture · Computer Science 2017-04-19 Wonil Choi , Mohammad Arjomand , Myoungsoo Jung , Mahmut Kandemir

We propose Sectored DRAM, a new, low-overhead DRAM substrate that reduces wasted energy by enabling fine-grained DRAM data transfers and DRAM row activation. Sectored DRAM leverages two key ideas to enable fine-grained data transfers and…

Many high end and next generation computing systems to incorporated alternative memory technologies to meet performance goals. Since these technologies present distinct advantages and tradeoffs compared to conventional DDR* SDRAM, such as…

Performance · Computer Science 2021-10-06 M. Ben Olson , Brandon Kammerdiener , Kshitij A. Doshi , Terry Jones , Michael R. Jantz

Analog compute-in-memory (CIM) in static random-access memory (SRAM) is promising for accelerating deep learning inference by circumventing the memory wall and exploiting ultra-efficient analog low-precision arithmetic. Latest analog CIM…

Hardware Architecture · Computer Science 2024-07-19 Zhiyu Chen , Ziyuan Wen , Weier Wan , Akhil Reddy Pakala , Yiwei Zou , Wei-Chen Wei , Zengyi Li , Yubei Chen , Kaiyuan Yang

Data movement in memory-intensive workloads, such as deep learning, incurs energy costs that are over three orders of magnitude higher than the cost of computation. Since these workloads involve frequent data transfers between memory and…

Hardware Architecture · Computer Science 2025-02-05 Bahareh Khabbazan , Marc Riera , Antonio González

Sparse deep learning has reduced computation significantly, but its irregular non-zero data distribution complicates the data flow and hinders data reuse, increasing on-chip SRAM access and thus power consumption of the chip. This paper…

Hardware Architecture · Computer Science 2025-03-26 Kai-Chieh Hsu , Tian-Sheuan Chang