Xing Hu

TL;DR: This work describes the hybrid execution patterns of GCNs on Intel Xeon CPU and proposes a hardware design with two efficient processing engines to alleviate the irregularity of Aggregation phase and leverage the regularity of Combination phase, and designs a GCN accelerator using a hybrid architecture to efficiently perform GCNs.

TL;DR: This paper designs a series of contrast tests using different types of datasets (ANN-oriented and SNN-oriented), diverse processing models, signal conversion methods, and learning algorithms, and recommends the most suitable model for each scenario and highlights the urgent need to build a benchmarking framework for SNNs with broader tasks, datasets, and metrics.

TL;DR: DeepSniffer as discussed by the authors proposes a learning-based model extraction framework to obtain the complete model architecture information without any prior knowledge of the victim model, which is robust to architectural and system noises introduced by the complex memory hierarchy and diverse run-time system optimizations.

TL;DR: This work is the first to propose such accurate model extraction techniques and demonstrate an end-to-end attack experimentally in the context of an off-the-shelf Nvidia GPU platform with full system stack.

TL;DR: A unified model description framework and a unified processing architecture (Tianjic), which covers the full stack from software to hardware, and a compatible routing infrastructure that enables homogeneous and heterogeneous scalability on a decentralized many-core network.