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Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference., , , , , , , , , and 1 other author(s). CoRR, (2023)Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training., , , , , , and . CoRR, (2024)Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators., , , , , , , , , and 3 other author(s). CoRR, (2023)Training LSTM Networks with Resistive Cross-Point Devices., , and . CoRR, (2018)Design principles of the sparse coding network and the role of "sister cells" in the olfactory system of Drosophila., , , and . Frontiers Comput. Neurosci., (2013)Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation., , , , , , , , , and 5 other author(s). CoRR, (2024)Training large-scale ANNs on simulated resistive crossbar arrays., , and . CoRR, (2019)A Phenomenological Synapse Model for Asynchronous Neurotransmitter Release., , , , , and . Frontiers Comput. Neurosci., (2015)Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices., , , , , , , , , and 1 other author(s). Frontiers Comput. Neurosci., (2021)Training Large-Scale Spiking Neural Networks on Multi-core Neuromorphic System Using Backpropagation., , , , , , , , and . ICONIP (3), volume 11955 of Lecture Notes in Computer Science, page 185-194. Springer, (2019)