Spin Transfer Torque-Magnetic Random Access Memory (STT-MRAM), as a new non-volatile memory technology with lower leakage power and higher density, is widely considered to be a new generation of memory technology that may replace SRAM in the cache. STT-MRAM is divided into Single-Level Cell (SLC) STT-MRAM and Multi-Level Cell (MLC) STT-MRAM. Compared with SLC STT-MRAM, MLC STT-MRAM has further improved its storage density. However, MLC STT-MRAM has a high energy consumption and write latency due to its unique two-step state transitions (TTs) issue. State-of-the-art approaches mitigate this issue by eliminating TTs with expansion coding methods. Unfortunately, they focus more on eliminating TTs and have limited improvement in reducing energy consumption. To this end, we propose a new scheme, AEIS, which further reduces energy consumption while eliminating TTs. Our work begins with exploring the general rules of (M,N)-based expansion coding methods that eliminate TTs. Based on the discovered rules, the minimum energy coding method is found. To further improve energy efficiency, we segment the cache lines according to the data pattern. We only apply the expansion coding to those flipping segments to reduce the expansion coding overhead. The evaluation results show that AEIS can eliminate TTs in MLC STT-MRAM, reduce energy consumption by 28.5%, and increase the lifetime by 24.8%, while the total number of bits used for the cache only increases by 5.7%.

ABSTRACT In the context of federated learning, there are certain doubts about the credibility of cloud servers as third parties, as they have the potential to extract sensitive information from participants' local data from gradients. In addition, cloud servers may even resort to forging aggregation results, leading to the destruction of the global model and successfully avoiding detection mechanisms. Therefore, in building a secure federated learning system, it is crucial to ensure the privacy and aggregation correctness of upload gradients. This article proposes a secure and efficient federated learning privacy protection scheme TreePPFL (Tree Privacy Protection Federated Learning) based on hop‐by‐hop communication verifiability. By using single concealment technology to encrypt model parameters, the privacy of the uploaded gradient is protected. At the same time, a bidirectional verification scheme was designed, which applies a homomorphic hash algorithm to enable the cloud server to verify the legitimacy of the client, while also enabling the client to verify the aggregation correctness of the cloud server. The client transmits information through hop‐by‐hop communication, improving the training and verification efficiency of the cloud server and the entire federated learning system. The security of the scheme was verified through security analysis. The empirical experiment used two publicly available datasets, MNIST and CIFAR‐100, and compared them in iid and noniid scenarios. The results showed that the TreePPFL scheme exhibited superior performance compared to other schemes.

Object detection and recognition (OD&R) based on deep learning is a hot topic in the application of synthetic aperture radar (SAR). These methodologies based on deep learning are inherently data-driven, which means that their performance is subjected to the corresponding datasets. Although existing datasets have included some common targets collected from real-valued intensity SAR images, there still exist some limitations in terms of quantity, categories, diversities, and data domain. Hence, it is urgent to establish a large-quantity benchmark for fine-grained OD&R on complex-valued SAR images, which contains rich signal-domain features well coupled with classical physical modeling. In addition, considering the unique imaging characteristics and diverse imaging conditions, some important attribute information, such as incidence and attitude angles, is necessary to be attached. In this article, we propose a novel benchmark dataset with more than 340k instances for fine-grained OD&R based on single-look complex (SLC) SAR images, which is named FAIR-CSAR. We collected complex-valued SAR images with a resolution of 1–5 m from 175 entire images of Gaofen-3 covering 32 cities and multiple sea areas worldwide. All instances in the FAIR-CSAR are annotated by oriented bounding boxes (OBBs), covering five major categories and 22 subcategories. Compared with existing datasets dedicated to OD&R, the FAIR-CSAR dataset has four particular advantages: 1) it contains complex-valued SAR images from various acquisition modes and polarization modes, including full-scale signal-domain features for object recognition; 2) it is much larger than other existing OD&R datasets in terms of quantity of instances; 3) it provides more fine-grained category annotation and more detailed attribute information; and 4) it provides more challenging images with some common imaging phenomena, such as speckle noise and azimuth ambiguities. To establish a baseline adapted for SLC SAR images, a multidomain feature extraction and fusion network (MDNet) is proposed as a novel framework to mine detailed information underlying various domains. A series of state-of-the-art (SOTA) algorithms are applied on the FAIR-CSAR to build the fine-grained OD&R benchmark. Experimental results indicate that FAIR-CSAR is closer to practical application and more challenging than existing datasets for SAR images.

The dynamic flexible job shop scheduling problem (DFJSP) is frequently encountered in the modern manufacturing industry. As the intelligent manufacturing paradigm evolves, it is urgent to design a dynamic scheduling framework for handling the uncertainty and complexity in the real-time production line control. DFJSP aims to dynamically address new job random arrival so that the desired objective could be optimized, such as the minimization of makespan. The intractability of this problem can be directly reflected by the following two points. 1) the arrival times of new jobs are unknown in advance, so the scheduling framework needs to schedule them in real-time optimization; 2) Two optimization tasks, including job operation selection and machine assignment, have to be handled, which means multiple actions must be controlled simultaneously. This paper proposes a novel end-to-end hierarchical reinforcement learning framework to cope with the large-scale DFJSP. For generality, a higher-level layer is designed to automatically divide the DFJSP into a series of sub-problems with different scales, i.e., static FJSPs, which aims to achieve global optimization. Moreover, two lower-level layers are constructed to efficiently solve the sub-problems generated from the higher-level layer. One layer's policy based on the graph neural network is trained to schedule a job operation, and another policy based on the multi-layer perceptron is trained to assign a machine to process the job operation. Especially a Markov decision process (MDP), including state, action, and reward function, is designed for each layer of the hierarchy. Numerical experiments, including offline training and online testing, are conducted on several large-scale instances with diverse production configurations. The results verify the effectiveness of the proposed framework compared with the existing dynamic scheduling methods such as well-known dispatching rules and the existing heuristics.