How is the RNN LSTM affected by the the von neumann bottleneck?
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The Recurrent Neural Network (RNN) Long Short-Term Memory (LSTM) model is impacted by the von Neumann bottleneck due to limited memory capacity and data communication bandwidth, hindering the computational power required for complex tasks. To address this issue, researchers have proposed implementing LSTM with memristor crossbars, offering in-memory computing capabilities that circumvent the von Neumann bottleneck. Additionally, in the context of spiking neuronal network simulations, irregular memory access patterns and poor cache utilization due to spatial and temporal sparsity pose challenges on conventional computers . Techniques like software-induced prefetching and software pipelining have been explored to enhance cache performance and reduce simulation time by up to 50% on many-core systems, showcasing a way to overcome the limitations imposed by the von Neumann bottleneck .
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