Xiang Chen

Chenhui Xu, Fuxun Yu, Zirui Xu et al.

Recent progress in computer vision-oriented neural network designs is mostly driven by capturing high-order neural interactions among inputs and features. And there emerged a variety of approaches to accomplish this, such as Transformers and its variants. However, these interactions generate a large amount of intermediate state and/or strong data dependency, leading to considerable memory consumption and computing cost, and therefore compromising the overall runtime performance. To address this challenge, we rethink the high-order interactive neural network design with a quadratic computing approach. Specifically, we propose QuadraNet -- a comprehensive model design methodology from neuron reconstruction to structural block and eventually to the overall neural network implementation. Leveraging quadratic neurons' intrinsic high-order advantages and dedicated computation optimization schemes, QuadraNet could effectively achieve optimal cognition and computation performance. Incorporating state-of-the-art hardware-aware neural architecture search and system integration techniques, QuadraNet could also be well generalized in different hardware constraint settings and deployment scenarios. The experiment shows thatQuadraNet achieves up to 1.5$\times$ throughput, 30% less memory footprint, and similar cognition performance, compared with the state-of-the-art high-order approaches.

Duo Zhong, Bojing Li, Xiang Chen et al.

The increasing prevalence of adversarial attacks on Artificial Intelligence (AI) systems has created a need for innovative security measures. However, the current methods of defending against these attacks often come with a high computing cost and require back-end processing, making real-time defense challenging. Fortunately, there have been remarkable advancements in edge-computing, which make it easier to deploy neural networks on edge devices. Building upon these advancements, we propose an edge framework design to enable universal and efficient detection of adversarial attacks. This framework incorporates an attention-based adversarial detection methodology and a lightweight detection network formation, making it suitable for a wide range of neural networks and can be deployed on edge devices. To assess the effectiveness of our proposed framework, we conducted evaluations on five neural networks. The results indicate an impressive 97.43% F-score can be achieved, demonstrating the framework's proficiency in detecting adversarial attacks. Moreover, our proposed framework also exhibits significantly reduced computing complexity and cost in comparison to previous detection methods. This aspect is particularly beneficial as it ensures that the defense mechanism can be efficiently implemented in real-time on-edge devices.

Xiaolong Ma, Geng Yuan, Sheng Lin et al.

The state-of-art DNN structures involve intensive computation and high memory storage. To mitigate the challenges, the memristor crossbar array has emerged as an intrinsically suitable matrix computation and low-power acceleration framework for DNN applications. However, the high accuracy solution for extreme model compression on memristor crossbar array architecture is still waiting for unraveling. In this paper, we propose a memristor-based DNN framework which combines both structured weight pruning and quantization by incorporating alternating direction method of multipliers (ADMM) algorithm for better pruning and quantization performance. We also discover the non-optimality of the ADMM solution in weight pruning and the unused data path in a structured pruned model. Motivated by these discoveries, we design a software-hardware co-optimization framework which contains the first proposed Network Purification and Unused Path Removal algorithms targeting on post-processing a structured pruned model after ADMM steps. By taking memristor hardware constraints into our whole framework, we achieve extreme high compression ratio on the state-of-art neural network structures with minimum accuracy loss. For quantizing structured pruned model, our framework achieves nearly no accuracy loss after quantizing weights to 8-bit memristor weight representation. We share our models at anonymous link https://bit.ly/2VnMUy0.