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RAPIDNN: In-Memory Deep Neural Network Acceleration Framework., , , , , and . CoRR, (2018)SCRIMP: A General Stochastic Computing Architecture using ReRAM in-Memory Processing., , , , , , and . DATE, page 1598-1601. IEEE, (2020)Hardware-Software Co-design to Accelerate Neural Network Applications., , , and . ACM J. Emerg. Technol. Comput. Syst., 15 (2): 21:1-21:18 (2019)Stochastic-HD: Leveraging Stochastic Computing on Hyper-Dimensional Computing., , , , , and . ICCD, page 321-325. IEEE, (2021)Thermal-Aware Design and Management for Search-based In-Memory Acceleration., , , and . DAC, page 174. ACM, (2019)Efficient and Secure Learning across Memory Hierarchy. University of California, San Diego, USA, (2021)Sparsity Controllable Hyperdimensional Computing for Genome Sequence Matching Acceleration., , , , , and . VLSI-SoC, page 1-6. IEEE, (2023)Exploring Processing In-Memory for Different Technologies., , and . ACM Great Lakes Symposium on VLSI, page 201-206. ACM, (2019)FELIX: fast and energy-efficient logic in memory., , and . ICCAD, page 55. ACM, (2018)RMAC: Runtime Configurable Floating Point Multiplier for Approximate Computing., , , and . ISLPED, page 12:1-12:6. ACM, (2018)