# InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks

arXiv: https://arxiv.org/abs/2104.10853 · HTML: https://mlsanity.com/paper/2104.10853/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [PACT: Parameterized Clipping Activation for Quantized Neural Networks](https://mlsanity.com/paper/1805.06085/) (2018)
- [HAQ: Hardware-Aware Automated Quantization with Mixed Precision](https://mlsanity.com/paper/1811.08886/) (2018)
- [SkipNet: Learning Dynamic Routing in Convolutional Networks](https://mlsanity.com/paper/1711.09485/) (2017)
- [Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss](https://mlsanity.com/paper/1808.05779/) (2018)
- [Scalable Methods for 8-bit Training of Neural Networks](https://mlsanity.com/paper/1805.11046/) (2018)
- [Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.00090/) (2018)
- [AdaBits: Neural Network Quantization with Adaptive Bit-Widths](https://mlsanity.com/paper/1912.09666/) (2019)
- [AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks](https://mlsanity.com/paper/2006.08198/) (2020)
- [Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference](https://mlsanity.com/paper/1907.04523/) (2019)
- [FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training](https://mlsanity.com/paper/2012.13113/) (2020)
- [CPT: Efficient Deep Neural Network Training via Cyclic Precision](https://mlsanity.com/paper/2101.09868/) (2021)
- [DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures](https://mlsanity.com/paper/2002.11270/) (2020)
- [SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation](https://mlsanity.com/paper/2005.03403/) (2020)
- [Joint Neural Architecture Search and Quantization](https://mlsanity.com/paper/1811.09426/) (2018)
- [Switchable Precision Neural Networks](https://mlsanity.com/paper/2002.02815/) (2020)
- [GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware](https://mlsanity.com/paper/1808.01363/) (2018)

## Built on by (most cited first, 2 of 2)

- [2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency](https://mlsanity.com/paper/2109.05223/) (2021)
- [Progressive Multi-Ancestor Bit-Depth Distillation](https://mlsanity.com/paper/2610.04100/) (2026)

## Abstract (arXiv)

The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT) devices has motivated a tremendous demand for automated solutions to enable fast development and deployment of efficient (1) DNNs equipped with instantaneous accuracy-efficiency trade-off capability to accommodate the time-varying resources at IoT devices and (2) dataflows to optimize DNNs' execution efficiency on different devices. Therefore, we propose InstantNet to automatically generate and deploy instantaneously switchable-precision networks which operate at variable bit-widths. Extensive experiments show that the proposed InstantNet consistently outperforms state-of-the-art designs.