Pre-trained gaze models learn to identify useful patterns commonly found across users, but subtle user-specific variations (i.e., eyelid shape or facial structure) can degrade model performance. Test-time personalization (TTP) adapts pre-trained models to these user-specific domain shifts using only a few unlabeled samples. Efficient fine-tuning is critical in performing this domain adaptation: data and computation resources can be limited-especially for on-device customization. While popular parameter-efficient fine-tuning (PEFT) methods address adaptation costs by updating only a small set of weights, they may not be taking full advantage of structures encoded in pre-trained filters. To more effectively leverage existing structures learned during pre-training, we reframe personalization as a process to reweight existing features rather than learning entirely new ones. We present Attentive Low-Rank Filter Adaptation (Alfa) to adapt gaze models by reweighting semantic patterns in pre-trained filters. With Alfa, singular value decomposition (SVD) extracts dominant spatial components that capture eye and facial characteristics across users. Via an attention mechanism, we need only a few unlabeled samples to adjust and reweight pre-trained structures, selectively amplifying those relevant to a target user. Alfa achieves the lowest average gaze errors across four cross-dataset gaze benchmarks, outperforming existing TTP methods and low-rank adaptation (LoRA)-based variants. We also show that Alfa's attentive low-rank methods can be applied to applications beyond vision, such as diffusion-based language models.

Real-time personalized gaze estimation on AR/VR devices requires both accuracy and efficiency, especially when adapting to individual users with limited personal data. This task is challenging due to low-latency requirements, the presence of dataset biases from dominant gaze directions, and risk of catastrophic forgetting during adaptation. We present Distilled and Fine-Tuned (DFT) Gaze, a lightweight model for personalized gaze estimation. Distilled from a larger teacher model, DFT Gaze reduces model size while retaining essential visual features through knowledge distillation, without relying on gaze-specific supervision. During fine-tuning, it integrates gaze-specific supervision with Adapters, reaching 281K parameters for efficient adaptation and online updates on edge devices. To mitigate dataset biases and reduce catastrophic forgetting, we introduce a clustering-based sampling that balances gaze distribution for better generalization and improves adaptation to individual gaze patterns, even with only 5 personal images. DFT Gaze outperforms state-of-the-art methods on the MPIIFaceGaze dataset for personalized gaze estimation. Despite having the smallest model size at 281K parameters, it maintains low gaze errors across other datasets, including MPIIGaze, OpenEDS2020, and AEA. At 10× smaller than its teacher model, DFT Gaze achieves fast inference, a low parameter count, and effective adaptation, making it well-suited for real-time applications in resource-constrained environments.

Bayesian machine learning is useful for applications that may make high-risk decisions with limited, noisy, or unlabeled data, as it provides great data efficiency and uncertainty estimation. Building on previous efforts, this work presents CoopMC, an algorithm-architecture co-optimization for developing more efficient MCMC-based Bayesian inference accelerators. CoopMC utilizes dynamic normalization (DyNorm), LUT-based exponential kernels (TableExp), and log-domain kernel fusion (LogFusion) to reduce computational precision and shrink ALU area by 7.5× without noticeable reduction in model performance. Also, a Tree-based Gibbs sampler (TreeSampler) improves hardware runtime from $\mathcal{O}$(N) to $\mathcal{O}$(log(N)), an 8.7× speedup, and yields 1.9× better area efficiency than the existing state-of-the-art Gibbs sampling architecture. These methods have been tested on 10 diverse workloads using 3 different types of Bayesian models, demonstrating applicability to many Bayesian algorithms. In an end-to-end case study, these optimizations achieve a 33% logic area reduction, 62% power reduction, and 1.53× speedup over previous state-of-the-art end-to-end MCMC accelerators.