Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model

<table class="table-group" id="tab7"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">16-bit adder design</td><td class="align_center">MED</td><td class="align_center">NMED</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">GDI-AMBAII</td><td class="align_center">0.0064</td><td class="align_center">4.88 × 10<sup>−8</sup></td></tr><tr><td class="align_left">GDI-HSETAII</td><td class="align_center">0.0095</td><td class="align_center">7.25 × 10<sup>−8</sup></td></tr><tr><td class="align_left">Proposed GDI-EAHSETAII</td><td class="align_center">0.0048</td><td class="align_center">3.66 × 10<sup>−8</sup></td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Error characteristics of existing and proposed approximate adders.</div>

Computational Intelligence and Neuroscience

tab7

Table 7

Table 7: Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model 