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In this paper, we propose a novel algorithm for compressing neural networks to reduce the memory requirements by using blocked hashing techniques. By adding blocked constraints on top of the conventional hashing technique, the test error rate is maintained while the spatial locality for the computations is preserved. Using this scheme, the synaptic connections are compressed by at least an order (10×)...
In this work, we propose an energy-efficient hardware accelerator for Deep Neural Network (DNN) using Low Rank Approximation (LRADNN). Using this scheme, inactive neurons in each layer of the DNN are dynamically identified and the corresponding computations are then bypassed. Accordingly, both the memory accesses and the arithmetic operations associated with these inactive neurons can be saved. Therefore,...
It has become a challenge to organize and process large scale multi-dimensional data. In this paper, we present R-HBase, a multi-dimensional indexing framework for cloud computing environment. The R-HBase framework consists of storage layer and index layer. The storage layer supports high throughput and the index layer answers query efficiently. R-HBase is evaluated with synthetic data. R-HBase can...
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