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	<title>Automatic Generation of Realtime Machine Learning Architectures - Revision history</title>
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	<updated>2026-04-04T08:01:25Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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		<title>Cclab: Created page with &quot;{{StudentProjectTemplate |Summary=In this project, it is required to build a tool to generate a dataflow model and construct architectures for such algorithms, while minimizin...&quot;</title>
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		<updated>2020-10-08T16:14:16Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;{{StudentProjectTemplate |Summary=In this project, it is required to build a tool to generate a dataflow model and construct architectures for such algorithms, while minimizin...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;{{StudentProjectTemplate&lt;br /&gt;
|Summary=In this project, it is required to build a tool to generate a dataflow model and construct architectures for such algorithms, while minimizing latency or meeting a specific deadline under area and power constraints.&lt;br /&gt;
|Keywords=Real-time, Machine learning, Hardware Design, Dataflow &lt;br /&gt;
|TimeFrame=December 2020, May 2021&lt;br /&gt;
|References=Subbaraj, H., 2020. Using Dataflow for Machine Learning Inference.&lt;br /&gt;
&lt;br /&gt;
Anderson, J., Alkabani, Y. and El-Ghazawi, T., 2019. Towards Energy-Quality Scaling in Deep Neural Networks. IEEE Design &amp;amp; Test.&lt;br /&gt;
|Prerequisites=Contact :&lt;br /&gt;
Yousra Alkabani and Hazem Ali&lt;br /&gt;
|Supervisor=Yousra Alkabani, Hazem Ali&lt;br /&gt;
|Level=Master&lt;br /&gt;
|Status=Open&lt;br /&gt;
}}&lt;br /&gt;
Real-time machine learning algorithms have shown their importance in multiple domains. There are applications that require machine learning from a continuous input stream of data. Dataflow computational models can be suitable for modelling such algorithms for the following reasons.&lt;br /&gt;
A) They will show the algorithms&amp;#039; main building blocks and allow reconfiguration that helps in generating multiple hardware architectures to execute a specific machine learning model.&lt;br /&gt;
B) They exploit possible parallelism that can impact the algorithm performance positively.&lt;br /&gt;
In this project, it is required to build a tool to generate a dataflow model and construct architectures for such algorithms, while minimizing latency or meeting a specific deadline under area and power constraints. The tools should generate VHDL or Verilog code of such architectures to evaluate them.&lt;/div&gt;</summary>
		<author><name>Cclab</name></author>
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