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Found 5 papers, 0 papers with code
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge
Since models are typically quantized for edge deployment, recent work has investigated quantization-aware NAS (QA-NAS) to search for highly accurate and efficient quantized models.
BOMP-NAS: Bayesian Optimization Mixed Precision NAS
Bayesian Optimization Mixed-Precision Neural Architecture Search (BOMP-NAS) is an approach to quantization-aware neural architecture search (QA-NAS) that leverages both Bayesian optimization (BO) and mixed-precision quantization (MP) to efficiently search for compact, high performance deep neural networks.
An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural Network Systems using Virtual Models
End-to-end performance estimation and measurement of deep neural network (DNN) systems become more important with increasing complexity of DNN systems consisting of hardware and software components.
Automated design of error-resilient and hardware-efficient deep neural networks
It is thus desirable to exploit optimization potential for error resilience and efficiency also at the algorithmic side, e. g., by optimizing the architecture of the DNN.
Efficient Stochastic Inference of Bitwise Deep Neural Networks
Recently published methods enable training of bitwise neural networks which allow reduced representation of down to a single bit per weight.
