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Efficient Hyperdimensional Learning with Trainable, Quantizable, and Holistic Data Representation.

, , , and . DATE, page 1-6. IEEE, (2023)

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RAPIDNN: In-Memory Deep Neural Network Acceleration Framework., , , , , and . CoRR, (2018)Image Recognition Accelerator Design Using In-Memory Processing., , and . IEEE Micro, 39 (1): 17-23 (2019)Sparsity Controllable Hyperdimensional Computing for Genome Sequence Matching Acceleration., , , , , and . VLSI-SoC, page 1-6. IEEE, (2023)Sidekick: Near Data Processing for Clustering Enhanced by Automatic Memory Disaggregation., , , , , and . DAC, page 1-6. IEEE, (2023)Comprehensive Integration of Hyperdimensional Computing with Deep Learning towards Neuro-Symbolic AI., , , , , , and . DAC, page 1-6. IEEE, (2023)Hierarchical and Distributed Machine Learning Inference Beyond the Edge., , , , , and . ICNSC, page 18-23. IEEE, (2019)NNgine: Ultra-Efficient Nearest Neighbor Accelerator Based on In-Memory Computing., , and . ICRC, page 1-8. IEEE, (2017)OpenHD: A GPU-Powered Framework for Hyperdimensional Computing., , , and . IEEE Trans. Computers, 71 (11): 2753-2765 (2022)Efficient Sparse Processing in Smart Home Applications., , , , , , and . SenSys-ML, page 19-24. ACM, (2019)MPIM: Multi-purpose in-memory processing using configurable resistive memory., , and . ASP-DAC, page 757-763. IEEE, (2017)