Challenge_Page.html

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<div class="challenge-wrapper">
  <header class="challenge-header">
    <div class="ch-header-content">
      <div style="display: flex; align-items: flex-start; justify-content: space-between; width: 100%;">
        <div style="flex: 1 1 auto; min-width: 0;">
          <div class="ch-badge">
            <span class="ch-badge-dot"></span>
            <span>Bharat AI-SoC Student Challenge</span>
          </div>
          <h1>Bharat AI-SoC Student Challenge</h1>
          <p class="ch-tagline">
            A project-based virtual challenge to ignite innovation in AI-driven System-on-Chip (SoC) design.
          </p>
          <div class="ch-header-meta">
            <div class="ch-meta-pill">
              <span class="ch-meta-label">Mode</span>
              <span class="ch-meta-value">Virtual Project Challenge</span>
            </div>
            <div class="ch-meta-pill">
              <span class="ch-meta-label">Team Size</span>
              <span class="ch-meta-value">1–3 Students</span>
            </div>
            <div class="ch-meta-pill">
              <span class="ch-meta-label">Eligibility</span>
              <span class="ch-meta-value">Indian Institutes Only</span>
            </div>
          </div>
        </div>
        <div style="flex: 0 0 auto; display: flex; flex-direction: column; align-items: center; justify-content: center; gap: 20px; min-width: 200px; padding: 20px 0;">
          <img src="images/ARM LOGO - 2025 INK_150 ppi_RGB.jpg" alt="Arm Logo" style="height: 80px; width: auto; border-radius: 16px; box-shadow: 0 6px 32px 0 rgba(0,0,0,0.12); background: rgba(255,255,255,0.95); padding: 12px 20px;" />
          <img src="images/c2s_meity_logo.jpg" alt="C2S MeitY Logo" style="height: 140px; width: auto; border-radius: 20px; box-shadow: 0 8px 40px 0 rgba(0,0,0,0.14); background: rgba(255,255,255,0.95); padding: 8px;" />
        </div>
      </div>
    </div>
  </header>

    <div style="background: linear-gradient(135deg, #fee2e2 0%, #fecaca 100%); border-left: 4px solid #dc2626; padding: 20px 24px; margin: 24px 0; border-radius: 8px; box-shadow: 0 4px 12px rgba(220, 38, 38, 0.1);">
    <div style="display: flex; align-items: flex-start; gap: 12px;">
      <div style="flex-shrink: 0; margin-top: 2px;">
        <svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="#dc2626" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
          <circle cx="12" cy="12" r="10"></circle>
          <line x1="12" y1="8" x2="12" y2="12"></line>
          <line x1="12" y1="16" x2="12.01" y2="16"></line>
        </svg>
      </div>
      <div style="flex: 1;">
        <h3 style="margin: 0 0 8px 0; color: #991b1b; font-size: 18px; font-weight: 600;">Registration Closed</h3>
        <p style="margin: 0 0 8px 0; color: #7f1d1d; font-size: 15px; line-height: 1.5;">
          The registration period for the Bharat AI-SoC Student Challenge has ended.
        </p>
      </div>
    </div>
  </div>

  <main class="ch-main">
    <!-- LEFT COLUMN -->
    <div>
      <section class="ch-section">
        <h2>Objective</h2>
        <p>
          Enhance industry-relevant skills through project-based learning in the space of AI and SoC via an experiential mini-project.
        </p>
        <p>
          To ignite a culture of innovation by empowering students to ideate next-generation SoC solutions that unite AI
          and sustainability, leveraging Arm architecture — preparing them to shape the semiconductor future.
        </p>
      </section>

      <section class="ch-section">
        <h2>Eligibility Criteria</h2>
        <ol>
          <li>Participants must be Indian nationals.</li>
          <li>Participants must be associated with an Indian institute.</li>
          <li>Students must be nominated by their respective college.</li>
          <li>Teams of 1–3 students can apply.</li>
          <li>All team members must be from the same college.</li>
          <li>Team must have a Team Leader & Faculty Mentor.</li>
          <li>Participants must be willing to learn about Edge AI, Software-Hardware Co-design, and Embedded Systems.</li>
        </ol>
      </section>

      <section class="ch-section">
        <h2>Challenge Guidelines & Terms</h2>
        <ul>
          <li>Only eligible students can participate.</li>
          <li>Team size: 1–3 members.</li>
          <li>Only Team Leader registers for the team.</li>
          <li>Duplicate registration results in rejection.</li>
          <li>Registration details must be accurate.</li>
          <li>Mentoring by industry & academic experts  (virtual) & college mentors (local).</li>
          <li>Teams can reach out to mentors(experts) via support email id as mentioned.</li>
          <li>Mini project to be hosted on GitHub and submitted using the challenge submission form.</li>
          <li>All Challenge related notifications & updates will posted in this page.</li>
          <li>Winners must provide college ID & documents.</li>
          <li>Updates will be posted on this page.</li>
          <li>The selection of finalists and winners will be solely at the discretion of the organizers of this challenge.</li>
          <li>Support: <strong>support@armbharatchallenge.com</strong></li>
        </ul>

        <div class="ch-note">
          <strong>Note:</strong> Incorrect or duplicate entries may result in disqualification.
        </div>
      </section>
    </div>

    <!-- RIGHT COLUMN -->
     <div>
      <section class="ch-section">
        <h2>Detailed Timeline</h2>

        <table class="ch-table">
          <thead>
            <tr>
              <th>Activity</th>
              <th>Description</th>
              <th>Start</th>
              <th>End</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>Registration</td>
              <td>Interested students can register using the below registration link</td>
              <td>05 Jan 2026</td>
              <td>20 Jan 2026</td>
            </tr>
            <tr>
              <td>Mentoring Session</td>
              <td>An online mentoring session by industry & academic experts will be conducted whose details will be shared in this webpage & also mailed to registrants</td>
              <td>10 Jan 2026</td>
              <td>20 Jan 2026</td>
            </tr>
            <tr>
              <td>Project Submission</td>
              <td>Project Submission form will be shared with the participants</td>
              <td>10 Feb 2026</td>
              <td>15 Feb 2026</td>
            </tr>
            <tr>
              <td>Project Evaluation</td>
              <td>The submitted projects will be evaluated by the industry & academic experts & accordingly Finalists will be selected</td>
              <td>05 Feb 2026</td>
              <td>05 Mar 2026</td>
            </tr>
            <tr>
              <td>Finals</td>
              <td>The Finalists will be invited for a virtual meetup/hack to deliver a pitch</td>
              <td colspan="2" style="text-align: center;">Mid of March</td>
            </tr>
            <tr>
              <td>Winners Announcement</td>
              <td>The winning teams will be announced in this webpage with the details of the Rewards</td>
              <td colspan="2" style="text-align: center;">Mid of March</td>
            </tr>
          </tbody>
        </table>
      </section>

     <section class="ch-section">
        <h2>Support & Queries</h2>

        <p>For general support, email:</p>
        <p>
          <a href="mailto:support@armbharatchallenge.com" class="ch-email">
            support@armbharatchallenge.com
          </a>
        </p>

        <p class="ch-tech-note">
          For any technical queries with regards to the problem statements, please mail your queries to
          <a href="mailto:tech-queries@armbharatchallenge.com">
            tech-queries@armbharatchallenge.com
          </a>
          clearly mentioning the problem statement number in the subject line.
        </p>

        <p class="ch-small">All updates will be posted on this page.</p>
     </section>

      <section class="ch-section ch-faq-card" id="faqCard" style="cursor: pointer;">
        <h2>Frequently Asked Questions (FAQ)</h2>
        <p>Find answers to common questions about the Bharat AI-SoC Student Challenge.</p>
        <p class="ch-small" style="color: #7c3aed; font-weight: 500;">Click here to view FAQs →</p>
      </section>
    </div>
  </main>
</div>


<!-- FAQ Popup Modal -->
<div id="faqPopup" style="display:none; position:fixed; top:0; left:0; width:100%; height:100%; background:rgba(0,0,0,0.6); z-index:1100; justify-content:center; align-items:center;">
  <div style="background:#fff; padding:30px; border-radius:16px; max-width:700px; width:90%; max-height:85vh; overflow-y:auto; position:relative; box-shadow: 0 10px 40px rgba(0,0,0,0.2);">
    <button id="closeFaqBtn" style="position:absolute; top:15px; right:20px; background:none; border:none; font-size:28px; cursor:pointer; color:#6b7280;">&times;</button>
    <h2 style="color:#7c3aed; margin:0 0 10px; font-size:24px;">Frequently Asked Questions</h2>
    <p style="color:#6b7280; margin:0 0 25px; font-size:14px;">Click on a question to expand and view the answer.</p>
    
    <div class="faq-list">
      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q1: Who can participate in this Challenge?</span>
        </summary>
        <div class="ch-acc-body">
          <p>The Bharat AI-SoC Student Challenge is open to any undergraduate or post-graduate student, currently enrolled in a regular full-time course from a recognized higher education Institution or University in India, and having some basic understanding of AI, ML, SoC and Digital Logic Design.</p>
        </div>
      </details>

      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q2: Can a team have members from different institutions?</span>
        </summary>
        <div class="ch-acc-body">
          <p>No, all team members should be from the same institution, and from the same Campus.</p>
        </div>
      </details>

      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q3: Can a team have members from different branches and different semesters from the same institution?</span>
        </summary>
        <div class="ch-acc-body">
          <p>Yes, a team can comprise of members from different branches and semesters, as long as all the team members are from the same institution, and are currently enrolled in a regular full-time course from the said institution.</p>
        </div>
      </details>

      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q4: Can a team make changes to its team composition, members and/or problem statements allocated to them?</span>
        </summary>
        <div class="ch-acc-body">
          <p>Yes, a team has the option of changing the team composition, members and/or problem statements allocated to them. However, this option can be exercised only once (1), and should be exercised within fifteen (15) days of registering. Concerned teams will have to inform the organizers about their modification intention by sending an email to <a href="mailto:support@armbharatchallenge.com">support@armbharatchallenge.com</a>. Any decision regarding acceptance/rejection of any modification request from registered teams will be at the sole discretion of the organizers of this Challenge.</p>
        </div>
      </details>

      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q5: Can a Faculty Mentor provide mentorship support to more than one (1) participating team from the same institution?</span>
        </summary>
        <div class="ch-acc-body">
          <p>Yes, a Faculty Mentor can provide mentorship support to more than one (1) team from the same institution. However, it will be the responsibility of the participating team and the concerned Mentor to ensure that he/she is not overloaded with mentorship requests, and that the teams receive the requisite amount of guidance and support from their Mentor.</p>
        </div>
      </details>

      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q6: Can someone enrolled in a distance/part-time program/course, and currently engaged as a working professional participate in this Challenge?</span>
        </summary>
        <div class="ch-acc-body">
          <p>No, individuals pursuing a distance/part-time program/course, while engaged as a working professional, are not eligible to participate in this Challenge. It is open only to undergraduate or post-graduate students, currently enrolled in a regular full-time course from a recognized higher education Institution or University in India.</p>
        </div>
      </details>

      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q7: Is there a prize money for the winning teams?</span>
        </summary>
        <div class="ch-acc-body">
          <p>Yes, the Top-5 teams will receive a prize money of Indian Rupees (INR) One (1) Lakh each.</p>
          <p>Up to fifty (50) teams can be selected as Challenge Finalists, contingent upon the teams and projects meeting pre-defined qualification criteria. All members of teams selected as Finalists will receive Arm-branded Swags.</p>
        </div>
      </details>

      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q8: Will this Challenge be organized digitally or in-person mode?</span>
        </summary>
        <div class="ch-acc-body">
          <p>The Bharat AI-SoC Student Challenge is an exclusively virtual hackathon. There is no in-person component to the same.</p>
        </div>
      </details>

      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q9: Will the hardware needed to work on the projects by the participants be provided by the Challenge organizers?</span>
        </summary>
        <div class="ch-acc-body">
          <p>No special hardware or software should be necessary for this Challenge. It is expected that the institutions that the participants are enrolled in will have the necessary hardware and software required to work on projects pertaining to this Challenge. As such, we encourage the participants to work with their respective College/University Administration and their Faculty Mentors for any kind of hardware and/or software support requirements.</p>
        </div>
      </details>

      <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q10: What is the evaluation criteria for projects submitted as part of this Challenge?</span>
        </summary>
        <div class="ch-acc-body">
          <p>A set of metrics and criteria has been defined and will be used to evaluate the projects submitted as part of Bharat AI-SoC Student Challenge. Some key aspects that the Panel of Judges may look into include:</p>
          <ul>
            <li>Understanding of the Problem Statement and Alignment to Program Objective</li>
            <li>Technical Correctness of the Solution and Implementation Quality</li>
            <li>Novelty of the Idea and Solution Developed</li>
            <li>Use of ARM Tools in the Solution and Criticality of the same</li>
            <li>Solution Performance and Optimization</li>
            <li>Documentation Quality and Completeness</li>
            <li>Solution Demo and Presentation</li>
          </ul>
        </div>
      </details>

       <details class="ch-accordion">
        <summary>
          <span class="ch-acc-title">Q11: Can the participants participate in this challenge by applying for more than 1 more problem statement?</span>
        </summary>
        <div class="ch-acc-body">
          <p>No, the participants can apply for only 1 problem statement out of the given 5 problem statements in this challenge.</p>
        </div>
      </details>
    </div>
  </div>
</div>

<!--Project Section-->

<section class="ch-section ch-projects">
  <h2>Project Problem Statements</h2>
  <p class="ch-projects-intro">
    Choose one of the following problem statements as a starting point for your project. Click to expand each project and view suggested objectives
  </p>


  <!-- Problem 1 -->
  <details class="ch-accordion">
    <summary>
      <span class="ch-acc-title">Problem Statement 1</span>
      <span class="ch-acc-subtitle">
        Offline, Privacy-Preserving Hindi Voice Assistant on Raspberry Pi
      </span>
    </summary>
    <div class="ch-acc-body">
      <h3>Objective</h3>
      <p>
        Develop a low-latency, privacy-preserving voice assistant on an Arm-based SBC (e.g., Raspberry Pi) that
        processes Hindi voice commands entirely offline. The assistant should handle local queries (time, weather, etc.)
        using on-device ASR and TTS.
      </p>

      <h3>Project Description</h3>
      <p>
        Students will build an embedded speech pipeline performing:
      </p>
      <div style="margin:24px 0; display:flex; justify-content:center;">
        <iframe width="560" height="315"
          src="https://www.youtube.com/embed/6RYfIH39pI0"
          title="YouTube video"
          frameborder="0"
          allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
          allowfullscreen
          style="max-width:100%; border-radius:12px; box-shadow:0 6px 24px rgba(0,0,0,0.12);"></iframe>
      </div>
      <ul>
        <li>Speech-to-text using a lightweight ASR model (e.g., Coqui STT or fine-tuned wav2vec2 for Hindi).</li>
        <li>Command parsing and intent recognition in Python.</li>
        <li>Text-to-speech responses using local TTS (eSpeak-NG or Festival).</li>
        <li>End-to-end on the Raspberry Pi CPU with no cloud dependency.</li>
      </ul>

      <h3>Key Requirements</h3>
      <ul>
        <li>Hardware:
          <ul>
            <li>Raspberry Pi 4 (or similar Arm SBC).</li>
            <li>USB microphone.</li>
            <li>Speaker via 3.5 mm jack or HDMI.</li>
          </ul>
        </li>
        <li>Software:
          <ul>
            <li>Python with PyAudio for audio I/O.</li>
            <li>Coqui STT or fine-tuned wav2vec2 for ASR.</li>
            <li>eSpeak-NG or Festival for TTS.</li>
            <li>Custom Python logic for intent recognition and command execution.</li>
          </ul>
        </li>
      </ul>

      <h3>Performance Targets</h3>
      <ul>
        <li>Sub-2-second response time per command.</li>
        <li>Accurate recognition for 10–15 Hindi commands.</li>
        <li>Robust, fully offline operation.</li>
      </ul>

      <h3>Deliverables</h3>
      <ul>
        <li>Source code for the full voice assistant pipeline.</li>
        <li>Documentation of any model fine-tuning / optimization steps.</li>
        <li>Demo video showing responses to multiple commands.</li>
        <li>Short report on architecture, challenges in Hindi ASR/TTS, and performance metrics.</li>
      </ul>

      <h3>Learning Outcomes</h3>
      <ul>
        <li>Hands-on experience with embedded speech AI and offline ASR/TTS.</li>
        <li>Understanding challenges of regional language processing.</li>
        <li>Integrating ASR, simple NLP/intent logic, and TTS on a constrained platform.</li>
      </ul>
    </div>
  </details>

  <!-- Problem 2 -->
  <details class="ch-accordion">
    <summary>
      <span class="ch-acc-title">Problem Statement 2</span>
      <span class="ch-acc-subtitle">
        Touchless HCI for Media Control Using Hand Gestures on NVIDIA Jetson Nano
      </span>
    </summary>
    <div class="ch-acc-body">
      <h3>Objective</h3>
      <p>
        Create a touchless HCI system using an NVIDIA Jetson Nano that translates real-time hand gestures into media
        control commands (e.g., play/pause, volume) for a local player such as VLC.
      </p>

      <h3>Project Description</h3>
      <p>
        Students will use MediaPipe Hands (optimized for Arm CPU and Jetson GPU) to detect hand landmarks, then classify
        gestures and map them to keyboard shortcuts using Python libraries like <code>pynput</code> or <code>xdotool</code>.
      </p>
      <div style="margin:24px 0; display:flex; justify-content:center;">
        <iframe width="560" height="315"
          src="https://www.youtube.com/embed/PNdTXSF_-Ak"
          title="YouTube video"
          frameborder="0"
          allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
          allowfullscreen
          style="max-width:100%; border-radius:12px; box-shadow:0 6px 24px rgba(0,0,0,0.12);"></iframe>
      </div>

      <h3>Key Requirements</h3>
      <ul>
        <li>Hardware:
          <ul>
            <li>NVIDIA Jetson Nano Developer Kit.</li>
            <li>USB webcam.</li>
            <li>Monitor and standard peripherals.</li>
          </ul>
        </li>
        <li><strong>Software</strong>:
          <ul>
            <li>JetPack OS with CUDA support.</li>
            <li>Python, OpenCV, MediaPipe.</li>
            <li>Media player application (e.g., VLC).</li>
          </ul>
        </li>
      </ul>

      <h3>Performance Targets</h3>
      <ul>
        <li>&gt;90% gesture recognition accuracy in controlled lighting.</li>
        <li>&lt;200 ms end-to-end latency for gesture → action.</li>
        <li>Stable at ≥15 FPS.</li>
      </ul>

      <h3>Deliverables</h3>
      <ul>
        <li>Source code for gesture recognition and control logic.</li>
        <li>Defined gesture set and mapping table.</li>
        <li>Demo video of real-time media control.</li>
        <li>Report on design, model choice and performance analysis.</li>
      </ul>

      <h3>Learning Outcomes</h3>
      <ul>
        <li>Practical experience with real-time edge computer vision.</li>
        <li>Pipeline optimization for low-latency inference.</li>
        <li>Integrating AI perception with system-level control.</li>
      </ul>
    </div>
  </details>

  <!-- Problem 3 -->
  <details class="ch-accordion">
    <summary>
      <span class="ch-acc-title">Problem Statement 3</span>
      <span class="ch-acc-subtitle">
        Real-Time Road Anomaly Detection from Dashcam Footage on Raspberry Pi
      </span>
    </summary>
    <div class="ch-acc-body">
      <h3>Objective</h3>
      <p>
        Build an edge AI application on Raspberry Pi that processes dashcam footage in real-time to detect and log road
        anomalies such as potholes and unexpected obstacles.
      </p>

      <h3>Project Description</h3>
      <p>
        Students will choose a lightweight object detector (e.g., MobileNet-SSD, YOLOv5s), convert it to an
        edge-optimized format (TensorFlow Lite / ONNX Runtime), and integrate it with an OpenCV video pipeline.
        Detected anomalies should trigger timestamped logs or saved clips.
      </p>
      <div style="margin:24px 0; display:flex; justify-content:center;">
        <iframe width="560" height="315"
          src="https://www.youtube.com/embed/uWn6R82CrHw"
          title="YouTube video"
          frameborder="0"
          allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
          allowfullscreen
          style="max-width:100%; border-radius:12px; box-shadow:0 6px 24px rgba(0,0,0,0.12);"></iframe>
      </div>

      <h3>Key Requirements</h3>
      <ul>
        <li>Hardware:
          <ul>
            <li>Raspberry Pi 4 with proper cooling.</li>
            <li>Raspberry Pi Camera Module v2 or USB webcam.</li>
            <li>High-write-speed microSD card.</li>
          </ul>
        </li>
        <li>Software:
          <ul>
            <li>Raspberry Pi OS.</li>
            <li>Python, OpenCV.</li>
            <li>TensorFlow Lite / ONNX Runtime with a pre-trained, quantized detection model.</li>
          </ul>
        </li>
      </ul>

      <h3>Performance Targets</h3>
      <ul>
        <li>≥5 FPS near-real-time inference.</li>
        <li>High precision to reduce false positives in logging.</li>
        <li>Robust under varying lighting conditions.</li>
      </ul>

      <h3>Deliverables</h3>
      <ul>
        <li>Source code for video processing and inference pipeline.</li>
        <li>Optimized deployed model file (.tflite / .onnx).</li>
        <li>Demo video with anomaly detection on sample footage.</li>
        <li>Report on model choice, optimization and performance.</li>
      </ul>

      <h3>Learning Outcomes</h3>
      <ul>
        <li>Optimizing and deploying neural networks for edge video analytics.</li>
        <li>Experience with embedded vision pipelines.</li>
        <li>Understanding accuracy vs speed vs compute trade-offs on Arm platforms.</li>
      </ul>
    </div>
  </details>

  <!-- Problem 4 -->
  <details class="ch-accordion">
  <summary>
    <span class="ch-acc-title">Problem Statement 4</span>
    <span class="ch-acc-subtitle">
      Real-Time On-Device Speech-to-Speech Translation using SME2 and/or NEON on Arm CPU
    </span>
  </summary>
  <div class="ch-acc-body">

    <h3>Objective</h3>
    <p>
      Build a fully local, real-time speech-to-speech translation system optimized for Arm-based CPUs, leveraging SME2 where available (preferred) or NEON/NPU otherwise. The system must perform speech recognition, LLM-based translation or semantic rewriting, and speech synthesis entirely on-device, meeting mobile latency, power, and thermal constraints.
    </p>

    <h3>Project Description</h3>
    <p>
      Students will design and deploy a real-time, on-device speech-to-speech translation pipeline running on a smartphone with an Arm-powered CPU (preferably SME2-enabled devices such as OPPO Find X9 or vivo X300).
    </p>
    <p>
      The system captures continuous spoken audio in Language A, performs:
      <ol>
        <li>On-device speech-to-text (STT),</li>
        <li>LLM-based translation or semantic rewriting, and</li>
        <li>Text-to-speech (TTS) synthesis,</li>
      </ol>
      to produce natural, fluent spoken output in Language B. All inference must run locally with no cloud dependency, demonstrating efficient use of Arm CPU acceleration and mobile-friendly optimizations.
    </p>
    <div style="margin:24px 0; display:flex; justify-content:center;">
      <iframe width="560" height="315"
        src="https://www.youtube.com/embed/A1X9nIiAV1M"
        title="YouTube video"
        frameborder="0"
        allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
        allowfullscreen
        style="max-width:100%; border-radius:12px; box-shadow:0 6px 24px rgba(0,0,0,0.12);"></iframe>
    </div>

    <h3>Key Requirements</h3>
    <ul>
      <li>Hardware:
        <ul>
          <li>Arm-based smartphone CPU</li>
          <li>SME2-enabled device preferred; otherwise NEON-optimized CPU or optional onboard NPU</li>
          <li>Microphone and audio output (speaker or headphones)</li>
        </ul>
      </li>
      <li>Software:
        <ul>
          <li>Speech-to-Text (STT):
            <ul>
              <li>Small-footprint on-device ASR model</li>
              <li>Examples: Whisper-tiny (int8), Wav2Vec2-lite, Vosk</li>
            </ul>
          </li>
          <li>LLM-Based Translation / Rewrite:
            <ul>
              <li>Compact on-device LLM</li>
              <li>Examples: Phi-2 (int4/int8), Gemma-2B (int4)</li>
              <li>Supports either direct translation or semantic rewriting for fluency</li>
            </ul>
          </li>
          <li>Text-to-Speech (TTS):
            <ul>
              <li>Low-latency neural acoustic model and vocoder</li>
              <li>Examples: FastSpeech2 + HiFiGAN, VITS-lite</li>
            </ul>
          </li>
          <li><strong>No cloud inference permitted</strong></li>
          <li><strong>Quantization and Arm-specific optimizations required (SME2/NEON)</strong></li>
        </ul>
      </li>
    </ul>


    <h3>Performance Targets</h3>
    <ul>
      <li>Near real-time end-to-end latency suitable for conversational use</li>
      <li>Efficient on-device inference using quantized and optimized models</li>
      <li>Energy-aware operation to maintain acceptable thermal and power behavior on mobile SoCs</li>
      <li>Intelligible, natural-sounding synthesized speech output with minimal delay</li>
    </ul>

    <h3>Deliverables</h3>
    <ul>
      <li>Fully functional on-device speech-to-speech translation pipeline</li>
      <li>Demonstration running on an Arm-based smartphone</li>
      <li>Performance evaluation including latency, CPU utilization, and power considerations</li>
      <li>Documentation describing model choices, optimizations (SME2/NEON), and system architecture</li>
    </ul>

    <h3>Learning Outcomes</h3>
    <ul>
      <li>Understanding of end-to-end speech-to-speech AI pipelines</li>
      <li>Hands-on experience optimizing AI workloads for Arm CPUs</li>
      <li>Practical knowledge of model quantization and mobile inference constraints</li>
      <li>Insight into energy-efficient, low-latency system design for edge AI</li>
      <li>Exposure to SME2 and NEON optimization strategies on modern Arm platforms</li>
    </ul>
  </div>
</details>

  <!-- Problem 5 -->
  <details class="ch-accordion">
    <summary>
      <span class="ch-acc-title">Problem Statement 5</span>
      <span class="ch-acc-subtitle">
        Real-Time Object Detection Using Hardware-Accelerated CNN on Xilinx Zynq FPGA with Arm Processor
      </span>
    </summary>
    <div class="ch-acc-body">
      <h3>Objective</h3>
      <p>
        Design and implement a hardware-accelerated CNN inference system on a Xilinx Zynq SoC, leveraging FPGA fabric to achieve real-time object detection or image classification, and quantitatively demonstrate performance improvements over a CPU-only implementation.
      </p>
      

      <h3>Project Description</h3>
      <p>
        This project focuses on accelerating edge AI workloads on embedded platforms using hardware/software co-design. Students will implement a lightweight convolutional neural network (CNN) for object detection or image classification on a Xilinx Zynq SoC, which integrates an Arm processor with FPGA fabric.
      </p>
      <p>
        The system partitions functionality between the Arm core and FPGA:
        <ul>
          <li>The Arm core handles image capture, preprocessing, control logic, and post-processing.</li>
          <li>The FPGA fabric accelerates compute-intensive CNN operations such as convolution, activation, and pooling using Vitis HLS or Vivado.</li>
        </ul>
        The final system will perform real-time inference using either a live camera feed or a standard dataset, with detailed performance comparison against a software-only CPU implementation.
      </p>
      <div style="margin:24px 0; display:flex; justify-content:center;">
        <iframe width="560" height="315"
          src="https://www.youtube.com/embed/_ufBkxl1zxo"
          title="YouTube video"
          frameborder="0"
          allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
          allowfullscreen
          style="max-width:100%; border-radius:12px; box-shadow:0 6px 24px rgba(0,0,0,0.12);"></iframe>
      </div>
   

      <h3>Key Requirements</h3>
      <ul>
        <li>Hardware:
          <ul>
            <li>Xilinx Zynq-based development board</li>
            <li>Examples: Zynq-7000, ZCU104, ZedBoard</li>
            <li>Camera input (USB or onboard) or stored image dataset</li>
            <li>Display output or serial console for results</li>
          </ul>
        </li>
        <li>Software:
          <ul>
            <li>CNN Models:
              <ul>
                <li>Lightweight models such as Tiny-YOLO, MobileNet, or a custom 3-layer CNN</li>
              </ul>
            </li>
            <li>FPGA Design:
              <ul>
                <li>Vitis HLS or Vivado for CNN accelerator implementation</li>
                <li>Verilog or HLS C++ for hardware modules</li>
              </ul>
            </li>
            <li>Embedded Software:
              <ul>
                <li>Vitis / SDSoC for HW/SW co-design</li>
                <li>Optional PetaLinux</li>
                <li>OpenCV for image capture and preprocessing</li>
                <li>C++ or Python for control logic and system integration</li>
              </ul>
            </li>
          </ul>
        </li>
      </ul>
    

      <h3>Performance Targets</h3>
      <ul>
        <li>Real-time or near real-time inference on embedded hardware</li>
        <li>Minimum 2× speedup compared to software-only CNN execution on Arm CPU</li>
        <li>Measurable improvements in:
          <ul>
            <li>Latency</li>
            <li>Throughput</li>
            <li>Power efficiency</li>
            <li>Efficient use of FPGA resources (LUTs, BRAM, DSPs)</li>
          </ul>
        </li>
      </ul>
    

      <h3>Deliverables</h3>
      <ul>
        <li>Working FPGA-accelerated CNN prototype performing object detection or image classification</li>
        <li>Hardware/software co-design implementation running on a Zynq platform</li>
        <li>Performance comparison between:
          <ul>
            <li>CPU-only implementation</li>
            <li>Hardware-accelerated implementation</li>
          </ul>
        </li>
        <li>Documentation covering:
          <ul>
            <li>System architecture</li>
            <li>Design partitioning decisions</li>
            <li>Performance analysis (latency, throughput, resource usage, power)</li>
          </ul>
        </li>
        <li>Live demo or recorded demonstration of real-time inference</li>
      </ul>


      <h3>Learning Outcomes</h3>
      <ul>
        <li>Understanding of embedded edge AI and CNN inference pipelines</li>
        <li>Practical experience with FPGA-based acceleration using HLS</li>
        <li>Skills in Arm–FPGA hardware/software co-design</li>
        <li>Performance analysis and optimization of embedded systems</li>
        <li>Insight into trade-offs between flexibility, performance, and power in heterogeneous SoCs</li>
      </ul>
    </div>
  </details>
</section>

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