August 2024 - talks - lists.cs.princeton.edu

Kun Woo Cho will present his FPO "Programmable Smart Radio Environments: From Theory to Hardware Implementation" on October 1, 2024 at 3pm in CS 302.
by Gradinfo 25 Aug '24

25 Aug '24

Kun Woo Cho will present his FPO "Programmable Smart Radio Environments: From Theory to Hardware Implementation" on October 1 , 2024 at 3pm in CS 302. Examiner: Prof. Kyle Jamieson (Advisor, CS), Prof. Yasaman Ghasempour (ECE), Prof. Andrew Appel (CS) Reader: Prof. Wyatt Lloyd (CS), Prof. Omid Abari (UCLA), Dr. Ranveer Chandra (MSR) All are welcome to attend. Title: Programmable Smart Radio Environments: From Theory to Hardware Implementation Abstract: Today ’s wireless networks are undergoing a rapid transformation, scaling in traffic vol- ume, spectral efficiency, and radio count as never seen before. This thesis addresses the critical challenges emerging from this evolution in next-generation (NextG) wire- less networks, focusing on realizing three key services: enhanced mobile broadband (Gbps or above), ultra-reliable low latency communication (order of milliseconds), and massive machine type communication (up to one million per squared kilometer). To meet these diverse and demanding requirements, this thesis poses a central question: Can we build a smarter radio environment controlled and learned by soft- ware, configuring itself in real-time to meet different application needs? Current approaches to handle uncontrolled wireless signals are end-to-end. Unfortunately, sending and receiving endpoints are limited in their ability to shape this inherent propagation behavior. For instance, although multiple antennas can shape the beam pattern departing the sender, it cannot control how the resulting signals arrive at the receiver after traversing environmental obstacles. By focusing on changing the envi- ronment itself rather than the communication endpoints, this thesis offers a significant shift in design paradigms for modern wireless networks. First, millimeter-wave (mmWave) technology offers multi-Gbps data rates due to its wide spectral bands. However, the high frequency of mmWave signals makes them vulnerable to blockage by walls, people, and obstacles, significantly limiting their practical applications. This thesis introduces mmWall, a programmable smart surface deployed on buildings to fully control the mmWave radio environment. Com- prising over 4,000 sub-millimeter meta-materials, mmWall can steer signals through the surface or reflect them, bringing outdoor mmWave signals indoors and bypassing obstacles. Also, this thesis proposes Wall-Street, a smart surface installed on vehicles to provide seamless and reliable mmWave connectivity in high-mobility scenarios. Second, satellite networks uses constellations of thousands of satellites to provide low latency communication and global coverage. However, these networks face two significant challenges: outages due to transient blockage, and complications in beam alignment between users and satellites due to the use of different frequency sub-bands in the uplink and downlink directions. Extending the concept of a programmable smart radio to satellite communications, this thesis introduces Wall-E, a dual-band smart surface that mitigates signal blockage and enhances reliability of satellite-to- ground links, and Monolith, a smart surface that boosts inter-satellite link capacity. Third, while these smart surfaces address challenges at the physical layer, we also tackle issues at the link/MAC layer, particularly in massive Internet of Things (IoT) networks. This thesis introduces Cross-Link Channel Prediction (CLCP), a ma- chine learning technique that learns the radio environment and accordingly allocates networking resources to a large number of IoT devices. This AI-driven approach com- plements my programmable surfaces, creating a comprehensive smart radio solution for NextG networks.

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Yue Tan will present her FPO "FAASTEN: An Architecture and implementation for securing cloud applications" on September 5, 2024 at 11am in CS 302.
by Gradinfo 15 Aug '24

15 Aug '24

Yue Tan will present her FPO "FAASTEN: An Architecture and implementation for securing cloud applications" on September 5, 2024 at 11am in CS 302. The members of her committee are as follows: Amit Levy (advisor), Maria Apostolaki, Wyatt Lloyd Readers: Mae Milano and Marcus Peinado (Microsoft Research) All are welcome to attend. The FPO abstract follows below: Modern web applications have evolved into intricate networks of micro-applications, posing challenges in ensuring the security of user data confidentiality and integrity. In the current discretionary security paradigm, developers must enforce policies for every microapplication and all data paths that can arise at run-time. A promising alternative paradigm, decentralized information flow control (DIFC), suggests that the underlying system should enforce policies by tracking information flows. As the cloud has become a standard operating system for developing web applications, this paper proposes the first DIFC design for the cloud. We present Faasten, an architecture and implementation for securing cloud applications. Faasten includes a DIFC model and a FaaS-inspired system interface that implements the DIFC model. We provide a detailed interface security analysis and showcase how three representative cloud applications benefit from Faasten. We also show that Faasten introduces negligible latencies in controlling information flows and policy storage costs.

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Samuel Ginzburg will present his FPO "VectorVisor: A Binary Translation Scheme for Throughput-Oriented GPU Acceleration" on Thursday, August 29, 2024 in CS 302 at 1pm.
by Gradinfo 15 Aug '24

15 Aug '24

Samuel Ginzburg will present his FPO "VectorVisor: A Binary Translation Scheme for Throughput-Oriented GPU Acceleration" on Thursday, August 29, 2024 in CS 302 at 1pm. The members of his FPO committee follows below: Examiners: Michael Freedman (Advisor), Wyatt Lloyd, and Amit Levy Readers: Mae Milano and Mohammad Shahrad (UBC) All are welcome to attend. Please see abstract below. Beyond conventional graphics applications, general-purpose GPU acceleration has had significant impact on machine learning and scientific computing workloads. Yet, it has failed to see widespread use for server-side applications, which we argue is because GPU programming models offer a level of abstraction that is either too low-level (e.g., OpenCL, CUDA) or too high-level (e.g., TensorFlow, Halide), depending on the language. Not all applications fit into either category, resulting in lost opportunities for GPU acceleration. We introduce VectorVisor, a vectorized binary translator that enables new opportunities for GPU acceleration by introducing a novel programming model for GPUs. With VectorVisor, many copies of the same server-side application are run concurrently on the GPU, where VectorVisor mimics the abstractions provided by CPU threads. To achieve this goal, we demonstrate how to (i) provide cross-platform support for system calls and recursion using continuations and (ii) make full use of the excess register file capacity and high memory bandwidth of GPUs. We then demonstrate that our binary translator is able to transparently accelerate certain classes of computebound workloads, gaining significant improvements in throughput-per-dollar of up to 2.9× compared to Intel x86-64 VMs in the cloud, and in some cases match the throughput-per-dollar of native CUDA baselines.

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Kaifeng Lyu will present his FPO "Implicit Bias in Deep Learning Optimization: A Mathematical Examination" on Friday, August 9, 2024 at 10:30 AM in CS 402 and Zoom.
by CS Grad Department 02 Aug '24

02 Aug '24

Kaifeng Lyu will present his FPO " Implicit Bias in Deep Learning Optimization: A Mathematical Examination " on Friday, August 9, 2024 at 10:30 AM in CS 402 and Zoom. Location: Zoom link: https://princeton.zoom.us/j/99448789401 The members of Kaifeng ’s committee are as follows: Examiners: Sanjeev Arora (Adviser), Elad Hazan , Chi Jin Readers: Danqi Chen , Jason Lee A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Deep learning has achieved remarkable success in recent years, yet training neural networks often involves a delicate combination of guesswork and hyperparameter tuning. A critical aspect of this process is the “implicit bias” of optimization methods, where minor changes in the optimization setup—without affecting the small training loss at convergence—can drastically shift the solution to which the model converges, thereby affecting test performance. This dissertation presents a collection of results that mathematically characterize this implicit bias in various training regimes. The first part of this dissertation explores how gradient descent, even without explicit regularization, can converge to solutions that maximize the margin. Previous results have established the first-order optimality of margin for homogeneous neural networks in general, but the global optimality of margin is not guaranteed due to their non-convex nature. This dissertation provides in-depth theoretical analyses when data has simple structures: for linearly separable data, we present both positive and negative results on whether the global optimality of margin can be attained. Furthermore, we show how this margin-based view can be used to explain interesting generalization phenomena in training neural networks with or without explicit regularization, including the simplicity bias and grokking phenomena. The second part of the dissertation presents two results that capture the implicit biases induced by finite learning rate. Many existing analyses, including the margin-based ones in the first part, describe implicit biases that hold even when the learning rate is infinitesimal. However, practical implementations use finite learning rates, which have been empirically observed to benefit generalization. We analyze how full-batch GD with finite learning rates, combined with key training components like normalization layers and weight decay, create a bias towards flatter minima, which are positively correlated with better generalization. Additionally, we study the implicit bias in stochastic optimization and derive rigorous approximations for the dynamics of adaptive gradient methods like Adam and RMSprop via Stochastic Differential Equations (SDEs) to capture the effect of finite learning rates. Based on this, we also derive the square root scaling rule as a practical guideline for adjusting the optimization hyperparameters of adaptive gradient methods when changing batch size.

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