Search for: All records

Recent research has made significant progress in text-to-image editing, yet numerous areas remain under explored. In this work, we propose a novel application in the culinary arts, leveraging diffusion models to adjust a range of dishes into a variety of cuisines. Our approach infuses each dish with unique twists representative of diverse culinary traditions and ingredient profiles. We introduce the Cuisine Transfer task and a comprehensive framework for its execution, along with a curated dataset comprising over 1600 unique food samples at the ingredient level. Additionally, we propose three Cuisine Transfer task specific metrics to accurately evaluate our method and address common failure scenarios in existing image editing techniques. Our evaluations demonstrate that our method significantly outperforms baseline models on the Cuisine Transfer task 

Edge servers have recently become very popular for performing localized analytics, especially on video, as they reduce data traffic and protect privacy. However, due to their resource constraints, these servers often employ compressed models, which are typically prone to data drift. Consequently, for edge servers to provide cloud-comparable quality, they must also perform continuous learning to mitigate this drift. However, at expected deployment scales, performing continuous training on every edge server is not sustainable due to their aggregate power demands on grid supply and associated sustainability footprints. To address these challenges, we propose Us.as,´ an approach combining algorithmic adjustments, hardware-software co-design, and morphable acceleration hardware to enable the training of workloads on these edge servers to be powered by renewable, but intermittent, solar power that can sustainably scale alongside data sources. Our evaluation of Us.as on a real-world´ traffic dataset indicates that our continuous learning approach simultaneously improves both accuracy and efficiency: Us.as´ offers a 4.96% greater mean accuracy than prior approaches while our morphable accelerator that adapts to solar variance can save up to {234.95kWH, 2.63MWH}/year/edge-server compared to a {DNN accelerator, data center scale GPU}, respectively. 

This work focuses on forecasting future license usage for high-performance computing environments and using such predictions to improve the effectiveness of job scheduling. Specifically, we propose a model that carries out both short-term and long-term license usage forecasting and a method of using forecasts to improve job scheduling. Our long-term forecasting model achieves a Mean Absolute Percentage Error (MAPE) as low as 0.26 for a 12-month forecast of daily peak license usage. Our job scheduling experimental results also indicate that wasted work from jobs with insufficient licenses can be reduced by up to 92% without increasing the average license-using job completion times, during periods of high license usage, with our proposed license-aware scheduler.