Distributed learning requires a frequent communication of neuralnetwork update data. For this, we present a set of new compression tools, jointly called differential neural network coding (dNNC). dNNC is specifically...
详细信息
Distributed learning requires a frequent communication of neuralnetwork update data. For this, we present a set of new compression tools, jointly called differential neural network coding (dNNC). dNNC is specifically tailored to efficiently code incremental neuralnetwork updates and includes tools for federated BatchNorm folding (FedBNF), structured and unstructured sparsification, tensor row skipping, quantization optimization and temporal adaptation for improved context-adaptive binary arithmetic coding (CABAC). Furthermore, dNNC provides a new parameter update tree (PUT) mechanism, which allows to identify updates for different neuralnetwork parameter sub-sets and their relationship in synchronous and asynchronous neuralnetwork communication scenarios. Most of these tools have been included into the standardization process of the NNC standard (ISO/IEC 15938-17) edition 2. We benchmark dNNC in multiple federated and split learning scenarios using a variety of NN models and data including vision transformers and large-scale ImageNet experiments: It achieves compression efficiencies of 60% in comparison to the NNC standard edition 1 for transparent coding cases, i.e., without degrading the inference or training performance. This corresponds to a reduction in the size of the NN updates to less than 1% of their original size. Moreover, dNNC reduces the overall energy consumption required for communication in federated learning systems by up to 94%.
Video-based point cloud compression (V-PCC) utilizes video compression technology to efficiently encode dense point clouds providing state-of-the-art compression performance with a relatively small computation burden....
详细信息
Video-based point cloud compression (V-PCC) utilizes video compression technology to efficiently encode dense point clouds providing state-of-the-art compression performance with a relatively small computation burden. V-PCC converts 3-dimensional point cloud data into three types of 2-dimensional frames, i.e., occupancy, geometry, and at-tribute frames, and encodes them via video compression. On the other hand, the quality of these frames may be degraded due to video com-pression. This paper proposes an adaptive neuralnetwork-based post -processing filter on attribute frames to alleviate the degradation problem. Furthermore, a novel training method using occupancy frames is studied. The experimental results show average BD-rate gains of 3.0%, 29.3% and 22.2% for Y, U and V respectively.
暂无评论