Users are beginning to form relationships with conversational large language models (LLMs) and smartphones will leverage neural processing that delivers performance supporting on-device inference.

while keeping active parameters low during inference. • Apple’s OpenELM suite, with models ranging from 270 million to 3 billion parameters, is optimized for iOS devices. While this ensures ...

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Abstract: Advances in deep learning (DL) have enabled the integration of intelligence into low-end Internet-of-things (IoT) devices. However, traditional DL inference on resource-constrained ...

The quantile varying coefficient model is robust to data heterogeneity, outliers and heavy-tailed distributions in the response variable. In addition, it can flexibly model dynamic patterns of ...

Particularly, dynamic convolution has emerged as a promising solution to accelerate activity inference of deep networks on mobile devices. Exploiting spatial redundancy, such a dynamic strategy can ...