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Devanik21 / A-Machine-Learning-Approach-for-Optimal-Low-Power-VLSI

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The project uses an ML surrogate model (e.g., Random Forest) to instantly predict a decoder's PPA (Power, Performance, Area) based on design parameters optimizing trade-off, significantly boosting efficiency and enabling a faster, data-driven VLSI design flow .

machine-learningrandom-forestpower-gatingvlsi-designdigital-circuit-optimizationdecoder-designlow-power-electronicsppa-optimization
  • Updated Apr 24, 2026
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Devanik21 /
A-Machine-Learning-Approach-for-Optimal-Low-Power-VLSI

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The project uses an ML surrogate model (e.g., Random Forest) to instantly predict a decoder's PPA (Power, Performance, Area) based on design parameters optimizing trade-off, significantly boosting efficiency and enabling a faster, data-driven VLSI design flow .
machine-learningrandom-forestpower-gatingvlsi-designdigital-circuit-optimizationdecoder-designlow-power-electronicsppa-optimization
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Updated
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  • Python

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, 'i');
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Devanik21 /
A-Machine-Learning-Approach-for-Optimal-Low-Power-VLSI

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The project uses an ML surrogate model (e.g., Random Forest) to instantly predict a decoder's PPA (Power, Performance, Area) based on design parameters optimizing trade-off, significantly boosting efficiency and enabling a faster, data-driven VLSI design flow .
machine-learningrandom-forestpower-gatingvlsi-designdigital-circuit-optimizationdecoder-designlow-power-electronicsppa-optimization
  • 
Updated
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  • Python

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Devanik21 /
A-Machine-Learning-Approach-for-Optimal-Low-Power-VLSI

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The project uses an ML surrogate model (e.g., Random Forest) to instantly predict a decoder's PPA (Power, Performance, Area) based on design parameters optimizing trade-off, significantly boosting efficiency and enabling a faster, data-driven VLSI design flow .
machine-learningrandom-forestpower-gatingvlsi-designdigital-circuit-optimizationdecoder-designlow-power-electronicsppa-optimization
  • 
Updated
Apr 24, 2026
  • Python

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var __m = "youtube.com";
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Devanik21 /
A-Machine-Learning-Approach-for-Optimal-Low-Power-VLSI

Star
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The project uses an ML surrogate model (e.g., Random Forest) to instantly predict a decoder's PPA (Power, Performance, Area) based on design parameters optimizing trade-off, significantly boosting efficiency and enabling a faster, data-driven VLSI design flow .
machine-learningrandom-forestpower-gatingvlsi-designdigital-circuit-optimizationdecoder-designlow-power-electronicsppa-optimization
  • 
Updated
Apr 24, 2026
  • Python

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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
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Devanik21 /
A-Machine-Learning-Approach-for-Optimal-Low-Power-VLSI

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The project uses an ML surrogate model (e.g., Random Forest) to instantly predict a decoder's PPA (Power, Performance, Area) based on design parameters optimizing trade-off, significantly boosting efficiency and enabling a faster, data-driven VLSI design flow .
machine-learningrandom-forestpower-gatingvlsi-designdigital-circuit-optimizationdecoder-designlow-power-electronicsppa-optimization
  • 
Updated
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  • Python

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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
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Devanik21 /
A-Machine-Learning-Approach-for-Optimal-Low-Power-VLSI

Star
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The project uses an ML surrogate model (e.g., Random Forest) to instantly predict a decoder's PPA (Power, Performance, Area) based on design parameters optimizing trade-off, significantly boosting efficiency and enabling a faster, data-driven VLSI design flow .
machine-learningrandom-forestpower-gatingvlsi-designdigital-circuit-optimizationdecoder-designlow-power-electronicsppa-optimization
  • 
Updated
Apr 24, 2026
  • Python

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, 'i');
if (__m === '*' || __re.test(location.href)) {
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Devanik21 /
A-Machine-Learning-Approach-for-Optimal-Low-Power-VLSI

Star
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The project uses an ML surrogate model (e.g., Random Forest) to instantly predict a decoder's PPA (Power, Performance, Area) based on design parameters optimizing trade-off, significantly boosting efficiency and enabling a faster, data-driven VLSI design flow .
machine-learningrandom-forestpower-gatingvlsi-designdigital-circuit-optimizationdecoder-designlow-power-electronicsppa-optimization
  • 
Updated
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  • Python

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