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PUMA: Efficient and Low-Cost Memory Allocation and Alignment Support for Processing-Using-Memory Architectures.

, , , and . CoRR, (2024)

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Employing classification-based algorithms for general-purpose approximate computing., , , , and . DAC, page 70:1-70:6. ACM, (2018)Data clustering for efficient approximate computing., , , , , , , , and . Des. Autom. Embed. Syst., 24 (1): 3-22 (2020)Benchmarking a New Paradigm: Experimental Analysis and Characterization of a Real Processing-in-Memory System., , , , , and . IEEE Access, (2022)Casper: Accelerating Stencil Computations Using Near-Cache Processing., , , , , , and . IEEE Access, (2023)TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems., , , , , and . CoRR, (2023)PUMA: Efficient and Low-Cost Memory Allocation and Alignment Support for Processing-Using-Memory Architectures., , , and . CoRR, (2024)DaPPA: A Data-Parallel Framework for Processing-in-Memory Architectures., , , , and . CoRR, (2023)HARP: Practically and Effectively Identifying Uncorrectable Errors in Memory Chips That Use On-Die Error-Correcting Codes., , and . MICRO, page 623-640. ACM, (2021)Methodologies, Workloads, and Tools for Processing-in-Memory: Enabling the Adoption of Data-Centric Architectures., , , and . ISVLSI, page 261-266. IEEE, (2022)Casper: Accelerating Stencil Computation using Near-cache Processing., , , , , , and . CoRR, (2021)