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Found 3 papers, 1 papers with code
You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms
Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs).
HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search
In this paper, we present a novel multi-objective hardware-aware neural architecture search (NAS) framework, namely HSCoNAS, to automate the design of deep neural networks (DNNs) with high accuracy but low latency upon target hardware.
Hardware Aware Neural Architecture Search
Neural Architecture Search
Bringing AI To Edge: From Deep Learning's Perspective
To bridge the gap, a plethora of deep learning techniques and optimization methods are proposed in the past years: light-weight deep learning models, network compression, and efficient neural architecture search.
