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Creators/Authors contains: "Li, Yanyu"

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  1. Free, publicly-accessible full text available December 1, 2024
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  4. With the ever-increasing popularity of edge devices, it is necessary to implement real-time segmentation on the edge for autonomous driving and many other applications. Vision Transformers (ViTs) have shown considerably stronger results for many vision tasks. However, ViTs with the fullattention mechanism usually consume a large number of computational resources, leading to difficulties for realtime inference on edge devices. In this paper, we aim to derive ViTs with fewer computations and fast inference speed to facilitate the dense prediction of semantic segmentation on edge devices. To achieve this, we propose a pruning parameterization method to formulate the pruning problem of semantic segmentation. Then we adopt a bi-level optimization method to solve this problem with the help of implicit gradients. Our experimental results demonstrate that we can achieve 38.9 mIoU on ADE20K val with a speed of 56.5 FPS on Samsung S21, which is the highest mIoU under the same computation constraint with real-time inference. 
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    Free, publicly-accessible full text available June 1, 2024
  5. There have been many recent attempts to extend the successes of convolutional neural networks (CNNs) from 2-dimensional (2D) image classification to 3-dimensional (3D) video recognition by exploring 3D CNNs. Considering the emerging growth of mobile or Internet of Things (IoT) market, it is essential to investigate the deployment of 3D CNNs on edge devices. Previous works have implemented standard 3D CNNs (C3D) on hardware platforms, however, they have not exploited model compression for acceleration of inference. This work proposes a hardware-aware pruning approach that can fully adapt to the loop tiling technique of FPGA design and is applied onto a novel 3D network called R(2+1)D. Leveraging the powerful ADMM, the proposed pruning method achieves simultaneous high accuracy and significant acceleration of computation on FPGA. With layer-wise pruning rates up to 10× and negligible accuracy loss, the pruned model is implemented on a Xilinx ZCU102 FPGA board, where the pruned model achieves 2.6× speedup compared with the unpruned version, and 2.3× speedup and 2.3× power efficiency improvement compared with state-of-the-art FPGA implementation of C3D. 
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