As a new generation of edge devices, the integrated CPU/GPU architecture has opened up new opportunities for deploying different scale vision models. In order to reduce models’ inference time on the integrated devices, this article first compresses deep learning models using model quantization. The quantization process greatly reduces the computation requirements of a model, which enables its deployment on embedded development boards. However, quantization also leads to lower GPU resource utilization during inference on integrated devices. This insufficient utilization results in slower inference speed. To address this problem, this article first depicts the data flow of model inference within an integrated device. Secondly, this article implements a unified memory management between the CPU and GPU based on managed memory strategy. Finally, this article designs a thread-level stream scheduling method to improve GPU utilization and throughput during model inference in a pipeline way. Experimental results show that the proposed method achieves a 2x-10x improvement in throughput compared with the TensorRT’s default scheduling method, which is crucial for realizing real-time inference tasks on edge devices.

Due to the compact nature, hearing aids are often equipped with only a small number of microphones. Such a restriction brings a conflict between noise reduction and spatial cue retention in binaural beamformers (BFs). To alleviate this conflict, we design a parametric binaural (PaBi) BF from the viewpoint of auditory perception. In human hearing, the binaural cues are frequency selective, i.e., the interaural phase difference (IPD) dominates at low frequencies while the interaural level difference (ILD) dominates at high frequencies. Accordingly, we construct a set of parametric IPD and ILD constraints to establish a novel cost function, which is then solved by the semi-definite relaxation strategy. By adjusting the involved parameters, a good trade-off between noise reduction and spatial cue preservation can be achieved. Moreover, the PaBi BF breaks through the degree-of-freedom limitation of existing methods. Experimental results show the superiority of the proposed method.

While online medical diagnosis provides significant convenience to users, it also incurs the risk of privacy breaches, which inspired the emergence of various privacy-preserving online medical schemes. Nonetheless, existing schemes either compromise partial privacy to third parties or rely on cryptographic methods with high computational complexity. In particular, they do not anticipate user’s disputes to the extent that there is no audit process to guarantee the correctness of the diagnosis results and the fairness of the schemes. Consequently, we propose an efficient and privacy-preserving online medical diagnosis scheme based on additive secret sharing (ASS). First, the anonymity of the user is provided in the medical diagnosis process, which ensures that the cloud cannot link the diagnosis results to the user. Then, we devise a minimum value protocol and a range comparison protocol to enhance the security of the online diagnosis. In addition, considering user’s disputes that arise in realistic scenarios (e.g., malicious users may cheat the diagnosis system for personal benefits), we construct a blockchain-based audit process to detect user’s behaviors and settle controversies. Finally, we demonstrate the security and efficiency of the proposed scheme with theoretical analysis and experimental evaluation.