swiftware-lab.github.io/research.html at main · SwiftWare-Lab/swiftware-lab.github.io · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
<!DOCTYPE html>
<html lang="en">
<head>
        <!-- Google tag (gtag.js) -->
    <script async src="https://www.googletagmanager.com/gtag/js?id=G-RDMH3QY4NK"></script>
    <script>
      window.dataLayer = window.dataLayer || [];
      function gtag(){dataLayer.push(arguments);}
      gtag('js', new Date());

      gtag('config', 'G-RDMH3QY4NK');
    </script>


    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Research - SwiftWare Lab</title>
    <link rel="stylesheet" href="main.css">
    <link rel="preconnect" href="https://fonts.googleapis.com">
    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
    <link href="https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;700&family=Playfair+Display:wght@700&display=swap" rel="stylesheet">
</head>
<body>
    <header>
        <div class="container site-header">
            <div class="logo">
                <h1><a href="index.html">SwiftWare Lab</a></h1>
            </div>
            <nav>
                <ul>
                    <li><a href="index.html">Home</a></li>
                    <li><a href="research.html">Research</a></li> <li><a href="people.html">People</a></li>
                    <li><a href="publications.html">Publications</a></li>
                    <li><a href="contact.html">Contact</a></li>
                </ul>
            </nav>
        </div>
    </header>

    <section class="hero">
        <div class="container">
            <h2>Our Research</h2>
            <p>Exploring the frontiers of knowledge in computing systems.</p>
        </div>
    </section>

    <div class="container main-content">
        <main class="content-area">
            <div class="research-page-layout">
                <nav class="internal-nav">
                    <h2>Research Areas</h2>
                    <ul>
                        <li><a href="#computing-system">Computing Systems for Linear Algebra</a></li>
                        <li><a href="#robotics-ai">Compiler for Cyber-Physical Systems</a></li>
                        <li><a href="#big-data">Big Data Analytics and Machine Learning</a></li>
                    </ul>
                </nav>
                <div class="research-topics-content">
                    <section id="computing-system">
                        <h2>Research Topic 1: Computing Systems for Linear Algebra</h2>
                        <p>The advancement of computing systems, arguably the most pivotal enabler of human discovery, faces fundamental constraints from physical limitations and finite energy resources. Transistor density scaling, the primary methodology for increasing computing power for decades, is approaching saturation. Concurrently, the energy consumption of computing systems is outstripping energy production, contributing to carbon emissions and exacerbating climate change concerns. An effective solution involves the intelligent utilization of transistors through the integration of application-specific accelerators alongside existing generic processors, resulting in heterogeneous computing paradigms. However, a significant challenge arises from the tedious manual restructuring required to adapt current applications and algorithms for these diverse resources. Although automated systems, such as compilers, are crucial for facilitating application redevelopment for heterogeneous processors, they frequently lag behind the rapid proliferation of novel accelerators and the communication bottlenecks inherent in these architectures. This project seeks to develop integrated software and hardware systems designed to accelerate scientific and machine learning applications on future heterogeneous architectures, thereby achieving high-performance and resilient systems with sustainable energy consumption.</p>
 <!--                        <p>Current projects include:</p>
                        <ul>
                            <li>Developing quantum algorithms for drug discovery simulations.</li>
                            <li>Investigating quantum machine learning approaches for complex data analysis.</li>
                            <li>Building robust error-correction protocols for future quantum hardware.</li>
                        </ul> -->
<!--                         <p><em>Key Researchers: Dr. Jane Doe, Alice Smith (PhD Candidate)</em></p>
                        <a href="#" class="read-more-link">Learn More about this topic &raquo;</a> -->
                    </section>

                    <section id="robotics-ai">
                        <h2>Research Topic 2: Compiler for Cyber-Physical Systems</h2>
                        <p>This project aims to develop scalable AI and optimization software systems specifically for resource-constrained computing environments, such as edge devices and industrial controllers within cyber-physical systems (CPS). While optimization and AI methods, particularly machine learning (ML), are crucial for applications like drone navigation, autonomous driving, medical signal processing, and power flow optimization, they often struggle to meet real-time requirements on devices with limited memory, power, and computational capabilities. Current state-of-the-art frameworks are typically optimized for high-performance processors equipped with GPUs and specialized accelerators, which are rarely found in CPS devices. We propose to develop real-time software for CPS devices that is inherently aware of power consumption and physical constraints..</p>
<!--                         <p><em>Key Researchers: Dr. Bob Johnson, Charlie Brown (PhD Student)</em></p>
                        <a href="#" class="read-more-link">Learn More about this topic &raquo;</a> -->
                    </section>

                    <section id="big-data">
                        <h2>Research Topic 3: Big Data Analytics and Machine Learning</h2>
                        <p>This research project addresses the formidable challenges posed by the exponential growth of big data, particularly within critical domains like healthcare and complex scientific simulations. Our core objective is to advance knowledge discovery by developing novel AI models capable of extracting profound insights from these massive datasets, strategically leveraging the strengths of existing state-of-the-art models rather than starting from scratch. Beyond model creation, a significant thrust of our work involves analyzing big data to identify intrinsic patterns that enable substantial reductions in their memory footprint, thereby mitigating the strain on storage and processing resources. Concurrently, we are developing sophisticated techniques to reduce the memory size of the resulting AI models themselves without compromising their accuracy or predictive power, a crucial step for deploying advanced AI solutions efficiently in diverse, potentially resource-constrained, real-world applications.</p>

<!--                         <p><em>Key Researchers: Dr. Jane Doe, Diana Miller (Undergraduate Researcher)</em></p>
                        <a href="#" class="read-more-link">Learn More about this topic &raquo;</a> -->
                    </section>
                </div>
            </div>
        </main>

        <aside class="sidebar">
            <div id="news-feed-placeholder">
                </div>
        </aside>
    </div>

    <footer>
        <div class="container">
            <p>&copy; <span id="current-year-footer"></span> SwiftWare Lab. All rights reserved.</p>
            <p><a href="#">Privacy Policy</a> | <a href="#">Terms of Use</a></p>
            <p>Powered by HTML & CSS</p>
        </div>
    </footer>

    <script>
        // JavaScript to load news feed content (reused from other pages)
        function loadNewsFeed() {
            fetch('news-feed-snippet.html')
                .then(response => response.text())
                .then(data => {
                    document.getElementById('news-feed-placeholder').innerHTML = data;
                })
                .catch(error => console.error('Error loading news feed:', error));
        }

        // JavaScript for smooth scrolling (Optional, but good UX)
        document.querySelectorAll('.internal-nav a').forEach(anchor => {
            anchor.addEventListener('click', function (e) {
                e.preventDefault();

                document.querySelector(this.getAttribute('href')).scrollIntoView({
                    behavior: 'smooth'
                });
            });
        });

        // Run functions when the page loads
        document.addEventListener('DOMContentLoaded', function() {
            loadNewsFeed();
            // Update the year in the footer
            document.getElementById('current-year-footer').textContent = new Date().getFullYear();
        });
    </script>
</body>
</html>