The success of DNNs is attributed to three factors: stronger computing capacity, more complex neural networks, and more data. However, you’re usually unable to access these factors when applying DNNs to edge applications such as autonomous driving, augmented reality (AR) and virtual reality (VR), IoT, and so on. Training DNNs requires a large amount of data, which can be difficult to obtain. Edge devices such as mobile phones or IoT devices have limited computing capacity, which requires specialized and efficient DNNs. Due to the diversity and complexity of hardware devices, the enormous design space of DNNs, and prohibitive training costs, designing efficient DNNs for target devices is a challenging task.
Join Bichen Wu as he details recent works addressing these problems. SqueezeSegV2 is an efficient DNN for lidar point cloud segmentation for autonomous driving. Lidar point cloud data is extremely difficult to annotate, but you can bypass this by leveraging simulated data to train the network and adapting it to achieve a performance comparable to training on real data. Synetgy is a DNN model and hardware codesigned FPGA accelerator that achieves 16.9 times speedup over the previous state of the art. The final work Bichen explains is a differentiable neural architecture search (DNAS) framework for automatic DNN design. With a small computational cost (8 GPUs for one day), DNAS discovers a family of DNNs called FBNet that outperforms previous state-of-the-art models designed manually and automatically. For different target devices, DNAS automatically adapts DNN architectures accordingly to optimize for latency while maintaining accuracy.
Bichen Wu is a PhD candidate at UC Berkeley, where he focuses on deep learning, computer vision, and autonomous driving.
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