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    <title>verilog on K155LA3</title>
    <link>https://k155la3.blog/tags/verilog/</link>
    <description>Recent content in verilog on K155LA3</description>
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        <title>Building speech controlled robot with Tensil and Arty A7 - Part II</title>
        <link>https://k155la3.blog/2022/07/01/building-speech-controlled-robot-with-tensil-and-arty-a7-part2/</link>
        <pubDate>Fri, 01 Jul 2022 00:00:00 +0000</pubDate>
        <guid>https://k155la3.blog/2022/07/01/building-speech-controlled-robot-with-tensil-and-arty-a7-part2/</guid>
        <description>Introduction This is part II of a two-part tutorial in which we will continue to learn how to build a speech controlled robot using Tensil open source machine learning (ML) acceleration framework, Digilent Arty A7-100T FPGA board, and Pololu Romi Chassis. In part I we focused on recognizing speech commands through a microphone. Part II will focus on translating commands into robot behavior and integrating with the Romi chassis.
 System architecture Let’s start by reviewing the system architecture we introduced in Part I.</description>
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      <item>
        <title>Building speech controlled robot with Tensil and Arty A7 - Part I</title>
        <link>https://k155la3.blog/2022/06/26/building-speech-controlled-robot-with-tensil-and-arty-a7-part1/</link>
        <pubDate>Sun, 26 Jun 2022 00:00:00 +0000</pubDate>
        <guid>https://k155la3.blog/2022/06/26/building-speech-controlled-robot-with-tensil-and-arty-a7-part1/</guid>
        <description>Introduction In this two-part tutorial we will learn how to build a speech controlled robot using Tensil open source machine learning (ML) acceleration framework and Digilent Arty A7-100T FPGA board. At the heart of this robot we will use the ML model for speech recognition. We will learn how Tensil framework enables ML inference to be tightly integrated with digital signal processing in a resource constrained environment of a mid-range Xilinx Artix-7 FPGA.</description>
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      <item>
        <title>Tensil tutorial for YOLO v4 Tiny on Ultra96 V2</title>
        <link>https://k155la3.blog/2022/04/04/tensil-tutorial-for-yolo-v4-tiny-on-ultra96-v2/</link>
        <pubDate>Mon, 04 Apr 2022 00:00:00 +0000</pubDate>
        <guid>https://k155la3.blog/2022/04/04/tensil-tutorial-for-yolo-v4-tiny-on-ultra96-v2/</guid>
        <description>Introduction This tutorial will use Avnet Ultra96 V2 development board and Tensil open-source inference accelerator to show how to run YOLO v4 Tiny&amp;ndash;the state-of-the-art ML model for object detection&amp;ndash;on FPGA. The YOLO model contains some operations that Tensil does not support. These operations are in the final stage of processing and are not compute-intensive. We will use TensorFlow Lite (TF-Lite) to run them on the CPU to work around this. We will use the PYNQ framework to receive real-time video from a USB webcam and show detected objects on a screen connected to Display Port.</description>
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      <item>
        <title>Tensil tutorial for PYNQ Z1</title>
        <link>https://k155la3.blog/2022/03/13/tensil-tutorial-for-pynq-z1/</link>
        <pubDate>Sun, 13 Mar 2022 00:00:00 +0000</pubDate>
        <guid>https://k155la3.blog/2022/03/13/tensil-tutorial-for-pynq-z1/</guid>
        <description>Introduction This tutorial will use the PYNQ Z1 development board and Tensil&amp;rsquo;s open-source inference accelerator to show how to run machine learning (ML) models on FPGA. We will be using ResNet-20 trained on the CIFAR dataset. These steps should work for any supported ML model &amp;ndash; currently all the common state-of-the-art convolutional neural networks are supported. Try it with your model!
We&amp;rsquo;ll give detailed end-to-end coverage that is easy to follow.</description>
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      <item>
        <title>Tensil tutorial for Ultra96 V2</title>
        <link>https://k155la3.blog/2022/03/06/tensil-tutorial-for-ultra96-v2/</link>
        <pubDate>Sun, 06 Mar 2022 00:00:00 +0000</pubDate>
        <guid>https://k155la3.blog/2022/03/06/tensil-tutorial-for-ultra96-v2/</guid>
        <description>Introduction This tutorial will use the Avnet Ultra96 V2 development board and Tensil&amp;rsquo;s open-source inference accelerator to show how to run machine learning (ML) models on FPGA. We will be using ResNet-20 trained on the CIFAR dataset. These steps should work for any supported ML model &amp;ndash; currently all the common state-of-the-art convolutional neural networks are supported. Try it with your model!
We&amp;rsquo;ll give detailed end-to-end coverage that is easy to follow.</description>
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      <item>
        <title>Conway&#39;s Game of Life on FPGA</title>
        <link>https://k155la3.blog/2020/10/09/conways-game-of-life-on-fpga/</link>
        <pubDate>Fri, 09 Oct 2020 00:00:00 +0000</pubDate>
        <guid>https://k155la3.blog/2020/10/09/conways-game-of-life-on-fpga/</guid>
        <description>When learning a new programming language, I like having a well defined yet non-trivial problem to solve. Conway&amp;rsquo;s Game of Life (GoL) fits this definition. It has enough depth to uncover various tradeoffs. So naturally, when I picked up Chisel hardware description language (HDL), I wanted to build Game of Life in FPGA. It turned out to be a lot more interesting than in software. This post will follow my progress from writing Chisel and Verilog code to running GoL on Digilent Arty A7 and seeing live patterns on a VGA screen.</description>
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