Wei Niu, Mengshu Sun, et al.
AAAI 2021
Recent advances in AI culminate a shift in science and engineering away from strong reliance on algorithmic and symbolic knowledge towards new data-driven approaches. How does the emerging intelligent data-centric world impact research on real-time and embedded computing? We argue for two effects: (1) new chal- lenges in embedded system contexts, and (2) new opportunities for community expansion beyond the embedded domain. First, on the embedded system side, the shifting nature of computing towards data-centricity affects the types of bot- tlenecks that arise. At training time, the bottlenecks are generally data-related. Embedded computing relies on scarce sensor data modalities, unlike those com- monly addressed in mainstream AI, necessitating solutions for efficient learning from scarce sensor data. At inference time, the bottlenecks are resource-related, calling for improved resource economy and novel scheduling policies. Further ahead, the convergence of AI around large language models (LLMs) introduces additional model-related challenges in embedded contexts. Second, on the domain expansion side, we argue that community expertise in handling resource bottle- necks is becoming increasingly relevant to a new domain: the cloud environment, driven by AI needs. The paper discusses the novel research directions that arise in the data-centric world of AI, covering data-, resource-, and model-related challenges in embedded systems as well as new opportunities in the cloud domain.
Wei Niu, Mengshu Sun, et al.
AAAI 2021
Lee Martie, Jessie Rosenberg, et al.
ICSE 2023
Neil Thompson, Martin Fleming, et al.
IAAI 2024
Giovanni Mariani, Florian Scheidegger, et al.
ICML 2018