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Found 7 papers, 0 papers with code
On the Mitigation of Read Disturbances in Neuromorphic Inference Hardware
Our analysis show both a strong dependency on model characteristics such as synaptic activation and criticality, and on the voltage used to read resistance states during inference.
Improving Inference Lifetime of Neuromorphic Systems via Intelligent Synapse Mapping
We propose an architectural solution to extend the read endurance of RRAM-based neuromorphic systems.
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN).
On the Role of System Software in Energy Management of Neuromorphic Computing
Based on such formulation, we first evaluate the role of a system software in managing the energy consumption of neuromorphic systems.
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware
We propose eSpine, a novel technique to improve lifetime by incorporating the endurance variation within each crossbar in mapping machine learning workloads, ensuring that synapses with higher activation are always implemented on memristors with higher endurance, and vice versa.
Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware
Such current variations create a thermal gradient within each crossbar of the hardware, depending on the machine learning workload and the mapping of neurons and synapses of the workload to these crossbars.
Reliability-Performance Trade-offs in Neuromorphic Computing
A major source of voltage drop in a crossbar of these architectures are the parasitic components on the crossbar's bitlines and wordlines, which are deliberately made longer to achieve lower cost-per-bit.
