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Found 4 papers, 2 papers with code
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural Accelerators
We first perform neural architecture search to obtain a small set of optimal architectures for one accelerator candidate.
One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search
A key requirement of efficient hardware-aware NAS is the fast evaluation of inference latencies in order to rank different architectures.
Hardware Aware Neural Architecture Search
Neural Architecture Search
Scaling Up Deep Neural Network Optimization for Edge Inference
In the first approach, we reuse the performance predictors built on a proxy device, and leverage the performance monotonicity to scale up the DNN optimization without re-building performance predictors for each different device.
A Note on Latency Variability of Deep Neural Networks for Mobile Inference
Running deep neural network (DNN) inference on mobile devices, i. e., mobile inference, has become a growing trend, making inference less dependent on network connections and keeping private data locally.
