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Emergent ReRAM-based accelerators support in-memory computation to accelerate deep neural network (DNN) inference. Weight matrix pruning of DNNs is a widely used technique to reduce the size of DNN models, thereby reducing the resource and energy consumption of ReRAM-based accelerators. However, conventional works on weight matrix pruning for ReRAM-based accelerators have three major issues. First, they use heuristics or rules from domain experts to prune the weights, leading to suboptimal pruning policies. Second, they mostly focus on improving compression ratio, thus may not meet accuracy constraints. Third, they ignore direct feedback of hardware.In this paper, we introduce an automated DNN pruning and mapping framework, named Auto-prune. It leverages reinforcement learning (RL) to automatically determine the pruning policy considering the constraint of accuracy loss. The reward function of RL agents is designed using hardware's direct feedback (i.e., accuracy and compression rate of occupied crossbars). The function directs the search of the pruning ratio of each layer for a global optimum considering the characteristics of individual layers of DNN models. Then Auto-prune maps the pruned weight matrices to crossbars to store only nontrivial elements. Finally, to avoid the dislocation problem, we design a new data-path in ReRAM-based accelerators to correctly index and feed input to matrix-vector computation leveraging the mechanism of operation units. Experimental results show that, compared to the state-of-the-art work, Auto-prune achieves up to 3.3X compression rate, 3.1X area efficiency, and 3.3X energy efficiency with a similar or even higher accuracy. CCS CONCEPTS• Computer systems organization → Neural networks; • Hardware → Analysis and design of emerging devices and systems.

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As one of the most popular frameworks for large-scale analytics processing, Hadoop is facing two challenges: both applications and storage devices become heterogeneous. However, existing data placement and job scheduling schemes pay little attention to such heterogeneity of either application I/O requirements or I/O device capability, thus can greatly degrade system efficiencies. In this paper, we propose ASPS, an Application and Storage-aware data Placement and job Scheduling approach for Hadoop clusters. The idea is to place application data and schedule application tasks considering both application I/O requirements and storage device characteristics. Specifically, ASPS first introduces novel metrics to quantify I/O requirements of applications. Then, based on the quantification, ASPS places data of different applications to the preferred storage devices. Finally, ASPS tries to launch jobs with high I/O requirements on the nodes with the same type of faster devices to improve system efficiency. We have implemented ASPS in Hadoop framework. Experimental results show that ASPS can reduce the completion time of a single application by up to 36% and the average completion time of six concurrent applications by 27%, compared to existing data placement policies and job scheduling approaches.