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A Review of In-Memory Computing Architectures for Machine Learning Applications.

, , , , and . ACM Great Lakes Symposium on VLSI, page 89-94. ACM, (2020)

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Accelerating Adversarial Attack using Process-in-Memory Architecture., , , , , , , and . MSN, page 325-330. IEEE, (2022)Heterogeneous Multi-Functional Look-Up-Table-based Processing-in-Memory Architecture for Deep Learning Acceleration., , , and . ISQED, page 1-8. IEEE, (2023)POLAR: Performance-aware On-device Learning Capable Programmable Processing-in-Memory Architecture for Low-Power ML Applications., , , , and . DSD, page 889-898. IEEE, (2022)Coarse-Grained High-speed Reconfigurable Array-based Approximate Accelerator for Deep Learning Applications., , and . CISS, page 1-6. IEEE, (2023)A Review of In-Memory Computing Architectures for Machine Learning Applications., , , , and . ACM Great Lakes Symposium on VLSI, page 89-94. ACM, (2020)Empowering Malware Detection Efficiency within Processing-in-Memory Architecture., , and . CoRR, (2024)Energy Harvesting-assisted Ultra-Low-Power Processing-in-Memory Accelerator for ML Applications., , and . ACM Great Lakes Symposium on VLSI, page 633-638. ACM, (2024)uPIM: Performance-aware Online Learning Capable Processing-in-Memory., , , and . AICAS, page 1-4. IEEE, (2021)FlutPIM: : A Look-up Table-based Processing in Memory Architecture with Floating-point Computation Support for Deep Learning Applications., , , , and . ACM Great Lakes Symposium on VLSI, page 207-211. ACM, (2023)Reconfigurable Processing-in-Memory Architecture for Data Intensive Applications., , , and . VLSID, page 222-227. IEEE, (2024)