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JianyiCheng/HLS-benchmarks: HLS_Benchmarks_First_Release

Jianyi Cheng


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3561115", 
  "title": "JianyiCheng/HLS-benchmarks: HLS_Benchmarks_First_Release", 
  "issued": {
    "date-parts": [
      [
        2019, 
        12, 
        3
      ]
    ]
  }, 
  "abstract": "<p>First release of my HLS benchmarks. The benchmarks include:</p>\n\n<ul>\n\t<li><em>gSum</em> sums a number of polynomial results from the array elements that meet the given conditions where the difference between two elements from the arrays is non-negative.</li>\n\t<li><em>gSumIf</em> is similar to <em>gSum</em> but the SS function returns one of two polynomial expressions based on the value of the difference.</li>\n\t<li><em>sparseMatrixPower</em> performs dot product of two matrices, which skips the operation when the weight is zero.</li>\n\t<li><em>histogram</em> sums various weight onto the corresponding features but also in a sparse form.</li>\n\t<li><em>getTanh</em> performs the approximated function <em>tanh(x)</em> onto an array of integers using the CORDIC algorithm and a polynomial function.</li>\n\t<li><em>getTanh(double)</em> is similar to <em>getTanh</em> but uses an array of doubles.</li>\n\t<li><em>BNNKernel</em> is a small BNN kernel with LUT function as XOR.</li>\n</ul>", 
  "author": [
    {
      "family": "Jianyi Cheng"
    }
  ], 
  "version": "v1.0", 
  "type": "article", 
  "id": "3561115"
}
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