Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  provide a conversion technique from decision-trees to compact hash tables. evaluate the performance on FPGA and multi-core platforms, respectively. achieve high speedups using our techniques on various platforms. Decision-trees have been widely used in scientific computing. It is challenging to sustain high performance for large decision-trees. ,  present a conversion technique translating a generic decision-tree into multiple compact hash tables; the conversion technique does not depend on the depth or shape of the decision-tree. All the compact hash tables are searched individually; the outcomes from all the tables are merged into the final result. To evaluate the performance,  prototype our design on state-of-the-art FPGA and multi-core General Purpose Processors (GPPs). Experimental results show that, for a typical 92-leaf decision-tree,  achieve 533?Million Classifications Per Second (MCPS) throughput and 26?ns latency on FPGA, and 134?MCPS throughput and 239?ns latency on multi-core GPP.  sustain 6 × and 2.7 × speedups, respectively.
['Yun Rock Qu', 'Viktor K. Prasanna']
I am writing a research paper on a idea Compact hash tables for decision-trees
[{"from": "human", "value": "I am writing a research paper on a idea Compact hash tables for decision-trees"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  provide a conversion technique from decision-trees to compact hash tables. evaluate the performance on FPGA and multi-core platforms, respectively. achieve high speedups using our techniques on various platforms. Decision-trees have been widely used in scientific computing. It is challenging to sustain high performance for large decision-trees. ,  present a conversion technique translating a generic decision-tree into multiple compact hash tables; the conversion technique does not depend on the depth or shape of the decision-tree. All the compact hash tables are searched individually; the outcomes from all the tables are merged into the final result. To evaluate the performance,  prototype our design on state-of-the-art FPGA and multi-core General Purpose Processors (GPPs). Experimental results show that, for a typical 92-leaf decision-tree,  achieve 533?Million Classifications Per Second (MCPS) throughput and 26?ns latency on FPGA, and 134?MCPS throughput and 239?ns latency on multi-core GPP.  sustain 6 \u00d7 and 2.7 \u00d7 speedups, respectively."}]