Multitask Learning-Based Pipeline-Parallel Computation Offloading Architecture for Deep Face Analysis by Faris S. Alghareb, Balqees Talal Hasan

“…Nevertheless, the superior accuracy of a DNN is achieved at the expense of intensive computations and storage complexity, requiring custom expandable hardware, i.e., graphics processing units (GPUs). Interestingly, leveraging the synergy of parallelism and edge computing can significantly improve CPU-based hardware platforms. …”
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Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision by Lucas Waltz, Sushma Katari, Chaeun Hong, Adit Anup, Julian Colbert, Anirudh Potlapally, Taylor Dill, Canaan Porter, John Engle, John Engle, Christopher Stewart, Hari Subramoni, Scott Shearer, Raghu Machiraju, Osler Ortez, Laura Lindsey, Arnab Nandi, Sami Khanal

“…IntroductionAdvancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. …”
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Conservation in action: Cost-effective UAVs and real-time detection of the globally threatened swamp deer (Rucervus duvaucelii) by Ravindra Nath Tripathi, Karan Agarwal, Vikas Tripathi, Ruchi Badola, Syed Ainul Hussain

“…This framework operates on a local server, synchronizing with consumer-grade UAVs at a rate of 32 frames per second (fps) with 320 pixel resolution using a frame sampling technique, notably without requiring a dedicated Graphical Processing Unit (GPU). This deliberate choice of not using GPU underscores the commitment to cost-effectiveness and aligns with the research's purpose, prioritizing accessibility and affordability for broader scientific exploration. …”
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