Researchers and practitioners are exploiting Remote Direct Memory Access (RDMA) technology to improve the efficiency of distributed machine learning and meet the demands of data-center applications. RDMA requires lossless network link to fully unleash its power. RDMA Over Converged Ethernet (RoCE) v2 focuses on congestion control, but fails to achieve efficient packet loss recovery; Improved RoCE NIC (IRN) addresses this issue based on RoCEv2, but does not use the Priority-based Flow Control (PFC) to maintain the advantage of RoCEv2 in detecting congestion. This paper proposes a method of congestion detection and link control via feedback in RDMA transmission, namely Feedback Data Flow Control (FDFC), that does not rely on PFC. FDFC detects and controls the link condition in real time to achieve the goals of precise detection, congestion control, and efficient packet loss recovering.

FPGA-based acceleration has been emerged to avoid the cloud computing overload problem by accelerating the compute-intensive workload on edge networks. Though existing studies for FPGA-based edge acceleration have focused on optimizing the computing time, they did not address the burden of the FPGA-aided server under an enormous computing requests circumstance. The massive computing requests can cause delays in transmission and processing at the FPGA-aided server, leading to long response times and high system energy consumption. Therefore, we propose an emerging edge acceleration architecture based on FPGA cluster over the low-latency RDMA-based network. Preliminary simulation results demonstrate that our architecture is fast, scalable, and energy-efficient in comparison with the FPGA-aided servers cluster.

State-of-the-art image synthesis methods are mostly based on generative adversarial networks and require large dataset and extensive training. Although the model-inversion-oriented branch of methods eliminate the training requirement, the quality of the resulting image tends to be limited due to the lack of sufficient natural and class-specific information. In this paper, we introduce a novel strategy for high fidelity image synthesis with a single pretrained classification network. The strategy includes a class-conditional natural regularization design and a corresponding metadata collecting procedure for different scenarios. We show that our method can synthesize high quality natural images that closely follow the features of one or more given seed images. Moreover, our method achieves surprisingly decent results in the task of sketch-based image synthesis without training. Finally, our method further improves the performance in terms of accuracy and efficiency in the data-free knowledge distillation task.