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Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile
The Winograd-enhanced DSA achieves up to 1. 85x gain in energy efficiency and up to 1. 83x end-to-end speed-up for state-of-the-art segmentation and detection networks.
Dynamic ConvNets on Tiny Devices via Nested Sparsity
This work introduces a new training and compression pipeline to build Nested Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets) suited for inference tasks deployed on resource-constrained devices at the edge of the Internet-of-Things.
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