AI at the Edge: Qualcomm AI Research at NeurIPS 2024 with Arash Behboodi - #711
Dec 3, 2024
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Arash Behboodi, Director of Engineering at Qualcomm AI Research, discusses what's on the agenda for this year's NeurIPS conference. He highlights the challenges of differentiable simulation, particularly in wireless systems, and dives into how uncertainty quantification can enhance machine learning models through conformal prediction and entropy. Behboodi also previews innovative demos like on-device video editing and 3D content generation, showcasing Qualcomm's commitment to making cutting-edge AI accessible.
Differentiable simulation techniques can optimize the design of wireless systems by bridging statistical models with physical simulations.
Recent advancements in low-rank adaptation enable effective generative AI deployment on edge devices, enhancing on-device personalization without high computational costs.
Deep dives
Advancements in AI Simulators
The podcast discusses the complexities of forward and inverse problems in wireless system design, particularly the use of simulators. Forward problems involve using a model to project real-world behavior, while inverse problems require extracting a model from existing data. For example, optimizing antenna placement in a home for optimal coverage exemplifies an inverse problem, requiring iterative testing through various simulators. The discussion highlights the importance of differentiable models, which can streamline these processes and potentially close the gap between statistical and physical simulators.
Differentiable Simulation Surrogates and Applications
A workshop at the upcoming NeurIPS conference will focus on data-driven simulation surrogates and their applications across different domains. The workshop aims to gather machine learning professionals to address common challenges, such as accuracy versus complexity, and the role of differentiability in solving inverse problems. Recent advancements in drug discovery and weather prediction models demonstrate the versatility of these techniques. The session will also explore how uncertainty can be effectively characterized within simulation frameworks, leveraging insights from various successful projects.
Connecting Uncertainty and Information Theory
The discussion sheds light on a research paper that connects conformal prediction and information theory to better characterize uncertainty within machine learning models. Conformal prediction provides statistical guarantees of a model's accuracy by generating sets of outputs that include the true label, while information theory explores quantifying uncertainty through concepts like entropy. By bridging these concepts, researchers aim to enhance model training processes and improve how uncertainty is communicated in model outputs. The integrative approach may lead to more efficient algorithms that better quantify and manage uncertainty in real-world applications.
Generative AI and Edge Device Applications
The podcast highlights ongoing efforts to bring generative AI capabilities to edge devices, addressing the challenges of low resource environments. Techniques such as low-rank adaptation (LoRA) facilitate on-device personalization, allowing large models to be fine-tuned without the need for extensive computational power. Additionally, advancements like sparse high-rank adapters improve efficiency by allowing quick adaptations without full model retraining. The emphasis on edge deployment reinforces the goal of making robust generative AI applications accessible for diverse user interactions while reducing latency and resource demands.
Today, we're joined by Arash Behboodi, director of engineering at Qualcomm AI Research to discuss the papers and workshops Qualcomm will be presenting at this year’s NeurIPS conference. We dig into the challenges and opportunities presented by differentiable simulation in wireless systems, the sciences, and beyond. We also explore recent work that ties conformal prediction to information theory, yielding a novel approach to incorporating uncertainty quantification directly into machine learning models. Finally, we review several papers enabling the efficient use of LoRA (Low-Rank Adaptation) on mobile devices (Hollowed Net, ShiRA, FouRA). Arash also previews the demos Qualcomm will be hosting at NeurIPS, including new video editing diffusion and 3D content generation models running on-device, Qualcomm's AI Hub, and more!