Quantization reduces model size and speeds up inference time by reducing the number of bits required to represent weights or activations. In NNI, both post-training quantization algorithms and ...
Abstract: With ongoing advancements in natural language processing (NLP) and deep learning methods, the demand for computational and memory resources has considerably increased, which signifies the ...
Abstract: From a perspective of spatial quantization, this letter systematically investigates the advantages of reconfigurable reflectarrays (RRAs) designed with closely-spaced elements. Focused on ...
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