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Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases.

, , , , and . ISVLSI, page 273-278. IEEE, (2022)

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Employing classification-based algorithms for general-purpose approximate computing., , , , and . DAC, page 70:1-70:6. ACM, (2018)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)Methodologies, Workloads, and Tools for Processing-in-Memory: Enabling the Adoption of Data-Centric Architectures., , , and . ISVLSI, page 261-266. IEEE, (2022)HARP: Practically and Effectively Identifying Uncorrectable Errors in Memory Chips That Use On-Die Error-Correcting Codes., , and . MICRO, page 623-640. ACM, (2021)ABACuS: All-Bank Activation Counters for Scalable and Low Overhead RowHammer Mitigation., , , , , , , , and . USENIX Security Symposium, USENIX Association, (2024)Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud., , , , and . CoRR, (2022)Benchmarking a New Paradigm: An Experimental Analysis of a Real Processing-in-Memory Architecture., , , , , and . CoRR, (2021)Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases., , , , and . ISVLSI, page 273-278. IEEE, (2022)Operand size reconfiguration for big data processing in memory., , , , , and . DATE, page 710-715. IEEE, (2017)