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<meta name="author" content="Matej Sirovatka">
<title>NAS for Ternary Neural Networks</title>
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</head>
<body class="quarto-light">
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<div class="slides">
<section id="title-slide" class="quarto-title-block center">
<h1 class="title">NAS for Ternary Neural Networks</h1>
<div class="quarto-title-authors">
<div class="quarto-title-author">
<div class="quarto-title-author-name">
Matej Sirovatka
</div>
</div>
</div>
</section>
<section id="introduction" class="slide level2">
<h2>Introduction</h2>
<ul>
<li>Neural Architecture Search (NAS) for Ternary Neural Networks</li>
<li>Goal: Design an algorithm that can automatically find the best TNN architecture for a given task</li>
</ul>
</section>
<section id="task" class="slide level2">
<h2>Task</h2>
<ul>
<li>Breast Cancer dataset</li>
<li>Baseline from the default implementation of TNN</li>
<li>Power: 6.12kW</li>
<li>Accuracy: 98.05%</li>
<li>Area: 108103083.0 <span class="math inline">\(nm^2\)</span></li>
</ul>
</section>
<section id="implementation" class="slide level2">
<h2>Implementation</h2>
<ul>
<li>Utilizing DEAP library</li>
<li>NSGA-II, SPEA2 or NSGA-III selection</li>
<li>Chromosome encoding</li>
<li>Multi fitness - output is a function of input</li>
</ul>
<img data-src="media/encoding.png" class="r-stretch"></section>
<section id="experiments-and-hypotheses" class="slide level2">
<h2>Experiments and Hypotheses</h2>
<ul>
<li>Different evolutionary algorithms (NSGA-II, SPEA2, NSGA-III)</li>
<li>Probabilities of crossover and mutation</li>
<li>Population size</li>
<li>Number of generations</li>
<li>Extra objectives</li>
</ul>
</section>
<section id="spea2-vs-nsga-ii-vs-nsga-iii" class="slide level2">
<h2>SPEA2 vs NSGA-II vs NSGA-III</h2>
<ul>
<li>NSGA-II was the first choice, not too good</li>
<li>SPEA2 was very slow and could not find good solutions fast enough</li>
<li>NSGA-III because it is good at multi-objective optimization</li>
</ul>
</section>
<section id="spea2" class="slide level2">
<h2>SPEA2</h2>
<p><img data-src="media/spea2.png" style="height:50.0%"></p>
</section>
<section id="nsga-ii" class="slide level2">
<h2>NSGA-II</h2>
<p><img data-src="media/nsga2.png" style="height:50.0%"></p>
</section>
<section id="nsga-iii" class="slide level2">
<h2>NSGA-III</h2>
<p><img data-src="media/nsga3.png" style="height:50.0%"></p>
</section>
<section id="comparison-of-different-probabilities-of-crossover" class="slide level2">
<h2>Comparison of different probabilities of crossover</h2>
<img data-src="media/compare_cxpb.png" class="no-stretch quarto-figure quarto-figure-center r-stretch" style="width:50.0%"></section>
<section id="comparison-of-different-population-sizes" class="slide level2">
<h2>Comparison of different population sizes</h2>
<img data-src="media/compare_pop_size.png" class="no-stretch quarto-figure quarto-figure-center r-stretch" style="width:50.0%"></section>
<section id="results" class="slide level2">
<h2>Results</h2>
<ul>
<li>NSGA-III is the best performing algorithm (finds the best solution)</li>
<li>Best solution:
<ul>
<li>Accuracy: 98.55%</li>
<li>Area: 8439297.0 <span class="math inline">\(nm^2\)</span></li>
<li>Power: 0.5236 kW</li>
</ul></li>
</ul>
<p>A 10x improvement in power consumption with a slight increase in accuracy.</p>
</section>
<section id="problems-1" class="slide level2">
<h2>Problems 1</h2>
<ul>
<li>Wrong choice of chromosome encoding - algorithm gets stuck permuting the same architecture</li>
<li>Low chance of finding harder to find optimal solutions</li>
</ul>
</section>
<section id="problems-2" class="slide level2">
<h2>Problems 2</h2>
<img data-src="media/pareto.png" class="no-stretch quarto-figure quarto-figure-center r-stretch" style="width:50.0%"><p><em>Pareto front showing the trade-offs between accuracy, area, and power consumption across solutions</em></p>
</section>
<section id="short-summary" class="slide level2">
<h2>Short summary</h2>
<ul>
<li>Accuracy increased by 0.5%</li>
<li>Area decreased by ~20%</li>
<li>Power decreased by ~90%</li>
</ul>
<p>Thank you for your attention!</p>
</section>
</div>
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