Under the threat of quantum computers, the public-key cryptosystems need to transition to the Post-Quantum Cryptography (PQC) standards. However, this migration process is hindered by the diverse mathematical structures of PQC standards as well as the side-channel attacks, especially for the resource-constrained and physically accessible edge devices. To address this issue, we present ARV-Q, an adaptive RISC-V vector processor that efficiently offers unified support of PQC standards and side-channel security enhancement. Firstly, we propose an adaptive RISC-V-based computing paradigm to adapt to PQC algorithms across diverse mathematical bases, the core of which is a crypto extension supporting all PQC standards plus schemes in the fourth round in NIST standardization process. This crypto extension can well cooperate with the RISC-V Vector Extension (RVV) and is capable of adaptive operator-type support. Second, we design a highly resource-efficient vector crypto engine featuring versatile Butterfly Units and multi-Selected-Element-Width-adaptive modular arithmetic constructs, achieving high hardware utilization with configurable parameter settings. The crypto engine is integrated into the RISC-V core via an agile extension interface capable of bridging all types of register transfers. Besides, due to the capability of cooperative hybrid vector computing of RVV and crypto extension, ARV-Q can flexibly adapt to side-channel attacks with no hardware overhead while maintaining high performance. ARV-Q is implemented in 22nm process, and post-layout simulations are conducted. Results outperform the state-of-the-art counterparts in primary PQC standards with 1.21-5.14× better latency and more than 4.41× better area efficiency.

The National Institute of Standards and Technology (NIST) has selected FALCON as one of the standardized digital signature algorithms against quantum attacks in 2022. Compared with the other post-quantum cryptography (PQC) schemes, lattice-based FALCON is more appropriate for future Internet of Things (loT) applications due to the fastest signature verification process and the lowest transmission overhead. In this paper, we propose a custom extension based on the RISC-V scalar-vector framework for efficient implementation of FALCON. To our best knowledge, this work is the first hardware-software co-design for complete FALCON signature generation and verification routines. Besides, we design the first FALCON Gaussian sampling hardware and a RISC-V vector extension (RVV) based domain-specific core. The proposed architecture accelerates kernel operations in FALCON, such as discrete Gaussian sampling, number theoretic transform (NTT), inverse NTT, and polynomial operations. Compared with the reference implementation, results on the gem5-RTL simulation platform present a speedup for signature generation and verification of up to 18 x and 6.9 x.

In order to improve the classification accuracy of analog circuit failure modes, this paper proposes an ultra-lightweight subspace attention module (ULSAM) classification method, which combines lightweight (reducing parameters) with attention mechanism to improve convolutional neural networks (CNN) feature extraction and classification performance. This article uses depthwise separable (DWS) convolution, by decomposing the standard convolution into depthwise convolution (feature extraction) and pointwise convolution (feature aggregation). Meanwhile, the attention mechanism is applied, only one 1×1 filter is used after depthwise convolution, which can compute efficient interaction of cross-channel information, and uses the linear relationship between feature maps to avoid the use of multi-layer perceptron (MLP). The application of the failure modes of analog circuits shows that the proposed ULSAM method can realize the pattern classification of analog circuit faults more quickly and accurately.

Understanding and mastering users role plays an important part in online public opinions tracking and electronic commerce marketing, etc. Different groups of users have different sociology attributes. Thus, it is very important and interesting to discover user role based on their sociology attributes. The present user role discovery methods are generally based on the structural features or static coarse-grained behavior features. In this paper, by analyzing a large number of real social network data, we propose a novel method for social role discovery based on sociology attributes features: we first mining and define several properties on behalf of sociology attributes; then, to deal with the sociology attributes features clustering, we use Bayesian information criterion as our stopping criterion; at last, the experimental results show that using this method can better understand user role in Sina Microblog. Besides, the methodology in this paper for user role discovery also can be applied to other social network in general.