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TraNNsformer: Neural Network Transformation for Memristive Crossbar based Neuromorphic System Design.

, , and . CoRR, (2017)

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HyperX: A Hybrid RRAM-SRAM partitioned system for error recovery in memristive Xbars., , , , , and . DATE, page 88-91. IEEE, (2022)Identifying Efficient Dataflows for Spiking Neural Networks., , and . ISLPED, page 2:1-2:6. ACM, (2022)NAX: neural architecture and memristive xbar based accelerator co-design., , , and . DAC, page 451-456. ACM, (2022)In-Memory Computing in Emerging Memory Technologies for Machine Learning: An Overview., , , , and . DAC, page 1-6. IEEE, (2020)Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges., , , , , , , , and . Proc. IEEE, 108 (12): 2276-2310 (2020)Neuromorphic Computing Across the Stack: Devices, Circuits and Architectures., , and . SiPS, page 1-6. IEEE, (2018)SPACE: Structured Compression and Sharing of Representational Space for Continual Learning., , , and . IEEE Access, (2021)NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems., , , and . CoRR, (2021)PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design., , , and . ICCAD, page 1-8. ACM, (2019)Energy-Efficient Target Recognition using ReRAM Crossbars for Enabling On-Device Intelligence., , , and . SiPS, page 1-6. IEEE, (2020)