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AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement
Designing suitable deep model architectures, for AI-driven on-device apps and features, at par with rapidly evolving mobile hardware and increasingly complex target scenarios is a difficult task.
Hybrid and Non-Uniform DNN quantization methods using Retro Synthesis data for efficient inference
The Hybrid-Quantization scheme determines the sensitivity of each layer for per-tensor and per-channel quantization, and thereby generates hybrid quantized models that are $10 - 20\%$ efficient in inference time while achieving same or better accuracy as compared to per-channel quantization.
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