Memory systems operating in high-radiation environments require robust protection against single-event effects (SEE) damage. While STT-MRAM offers inherent advantages due to its spin-based storage, conventional redundancy repair architectures remain vulnerable due to separated storage and configuration circuits, slow boot performance, and radiation-induced errors in long signal paths. This paper proposes a novel radiation-hardened one-time programmable (OTP) content-addressable memory (CAM) based on magnetic tunnel junctions (MTJs) for efficient column redundancy in STT-MRAM macros. The design incorporates a radiation-hardened-by-design (RHBD) CAM array with built-in self-repair (BISR), featuring complementary OTP MTJ bitcells enabling parallel programming and disturbance-free matching, a soft-error resilient array with dual-node hardened latches and dual match-line sensing, and a DDR4-compatible repair mechanism supporting TMR-Latch-based fast initialization and energy-efficient search with inter-loop termination. The proposed system significantly improves wake-up speed to less than two clock cycles, and reduces power consumption to less than 12fJ, offering a viable solution for 37MeV radiation-tolerant memory systems.

Last-Level Cache (LLC) is increasingly required to be energy-efficient and area-saving. Emerging Non-volatile memory (NVM), such as Magnetic-resistive Random Access Memory (MRAM), present potential solutions for LLC as its ultra-low leakage and area. However, the high replacement-latency caused by its high write-latency and power consumption hinders MRAM in LLC applications. Thus, this paper proposes a novel Sense-While-Replace (SWR) strategy for dual-port SOT-MRAM, which liberates the conflict between reading and writing to conceal the impact of high write-latency on system performance. Furthermore, Self-aware access circuits are proposed, which accelerate reading and obtain utmost writing-energy saving. Under 40-nm CMOS technology, the 4Kb Macro achieves <3ns@32bits read and <75% energy-saving. Most crucially, SWR supports CPU continuously read whereas preserved from replacement latency, which improves performance by up to 8% even compared to SRAM.

This brief proposes a novel read-write scheme for energy-efficient SOT-MRAM. First, a dual-path-based smart write scheme is introduced, which utilizes a low-leakage half Schmitt Trigger (LLH-ST) to enable both early judge termination and write complete termination in a dual write path. Based on the pulse-width-dependent critical switching current characteristics of SOT devices, we propose a dynamic gradient-ascent (DGA) write driver to mitigate write energy consumption under device level variations. For write operation, a feedback voltage-controlled negative differential resistance (FV-NDR) structure is applied. It enables voltage latching by comparing and feeding back the stored data, allowing rapid readout during the discharge phase and achieving low power consumption. Based on a 40nm CMOS technology, the proposed dual-path-based smart write scheme with DGA write driver achieves power savings of 86.27% at early judge termination and 67.64% at write complete termination, with no additional timing overhead compared to other write termination schemes. The proposed FV-NDR structure achieves 75.35% read power savings compared to traditional readout, offering a read window of 257.1 mV and enabling 1.9ns readout.

Spin–orbit torque (SOT)-MRAM offers a promising solution for computer vision (CV) applications through its superior throughput and bit-cell energy efficiency. Meanwhile, redundant features lead to dominant energy consumption in memory-access peripherals and analog-to-digital converters (ADCs). Existing delta-sigma in-memory computing ($\Delta \Sigma $IMC) only reduces logic switching power, which has limited energy benefit for non-volatile memory (NVM)-based computing cores. This paper proposes an SOT-MRAM-based$\Delta $-bias-$\Sigma $computing macro to address the critical energy bottlenecks in CV applications. Voltage-delta-sensitive multiplier (VDM) realizes in-SOT-MRAM multiplication of incremental values, while eliminating redundant memory-access power caused by repetitive features. Delta-sensitive analog biasing (DS-biasing) scheme reduces the required ADC dynamic range, while delta-input-adaptive (DA) variable resolution ADC eliminates unnecessary quantization energy from repetitive inputs. Simulations based on 32kb SOT-MRAM computing macro demonstrate that the VDM achieves$6.45\times $energy reduction over conventional MRAM compute unit, while the DS-biasing combined with DA-ADC achieves$1.8\times $A/D conversion energy reduction compared with$\Delta \Sigma $IMC. The CV computing system achieves 546TOPS/W/b on MNIST image classification task with 97.9% accuracy, while improving 34% and 5% energy efficiency in video edge detection task and MobileViT inference task, respectively.

Diffusion models have quickly become some of the most popular and powerful generative models for high-dimensional data. The key insight that enabled their development was the realization that access to the score---the gradient of the log-density at different noise levels---allows for sampling from data distributions by solving a reverse-time stochastic differential equation (SDE) via forward discretization, and that popular denoisers allow for unbiased estimators of this score. In this paper, we demonstrate that an alternative, backward discretization of these SDEs, using proximal maps in place of the score, leads to theoretical and practical benefits. We leverage recent results in _proximal matching_ to learn proximal operators of the log-density and, with them, develop Proximal Diffusion Models (`ProxDM`). Theoretically, we prove that $\widetilde{\mathcal O}(d/\sqrt{\varepsilon})$ steps suffice for the resulting discretization to generate an $\varepsilon$-accurate distribution w.r.t. the KL divergence. Empirically, we show that two variants of `ProxDM` achieve significantly faster convergence within just a few sampling steps compared to conventional score-matching methods.

Proximal operators are ubiquitous in inverse problems, commonly appearing as part of algorithmic strategies to regularize problems that are otherwise ill-posed. Modern deep learning models have been brought to bear for these tasks too, as in the framework of plug-and-play or deep unrolling, where they loosely resemble proximal operators. Yet, something essential is lost in employing these purely data-driven approaches: there is no guarantee that a general deep network represents the proximal operator of any function, nor is there any characterization of the function for which the network might provide some approximate proximal. This not only makes guaranteeing convergence of iterative schemes challenging but, more fundamentally, complicates the analysis of what has been learned by these networks about their training data. Herein we provide a framework to develop *learned proximal networks* (LPN), prove that they provide exact proximal operators for a data-driven nonconvex regularizer, and show how a new training strategy, dubbed *proximal matching*, provably promotes the recovery of the log-prior of the true data distribution. Such LPN provide general, unsupervised, expressive proximal operators that can be used for general inverse problems with convergence guarantees. We illustrate our results in a series of cases of increasing complexity, demonstrating that these models not only result in state-of-the-art performance, but provide a window into the resulting priors learned from data.

Recent studies on deepfake detection have achieved promising results when training and testing faces are from the same dataset. However, their results severely degrade when confronted with forged samples that the model has not yet seen during training. In this paper, deepfake data to help detect deepfakes. this paper present we put a new insight into diffusion model-based data augmentation, and propose a Masked Conditional Diffusion Model (MCDM) for enhancing deepfake detection. It generates a variety of forged faces from a masked pristine one, encouraging the deepfake detection model to learn generic and robust representations without overfitting to special artifacts. Extensive experiments demonstrate that forgery images generated with our method are of high quality and helpful to improve the performance of deepfake detection models.

Abstract Recently, image deblurring task driven by the encoder-decoder network has made a tremendous amount of progress. However, these encoder-decoder-based networks still have two disadvantages: (1) due to the lack of feedback mechanism in the decoder design, the reconstruction results of existing networks are still sub-optimal; (2) these networks introduce multiple modules, such as the self-attention mechanism, to improve the performance, which also increases the computational burden. To overcome these issues, this paper proposes a novel feedback-mechanism-based encoder-decoder network (namely, FMNet) that is equipped with two key components: (1) the feedback-mechanism-based decoder and (2) the dual gated attention module. To improve reconstruction quality, the feedback-mechanism-based decoder is proposed to leverage the feedback information via the feedback attention module, which adaptively selects useful features in the feedback path. To decrease the computational cost, an efficient dual gated attention module is proposed to perform the attention mechanism in the frequency domain twice, which improves deblurring performance while reducing the computational cost by avoiding redundant convolutions and feature channels. The superiority of FMNet in terms of both deblurring performance and computational efficiency is demonstrated via comparisons with state-of-the-art methods on multiple public datasets.

Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging technique that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor model. STI has the potential to provide information for both the reconstruction of white matter fiber pathways and detection of myelin changes in the brain at mm resolution or less, which would be of great value for understanding brain structure and function in healthy and diseased brain. However, the application of STI in vivo has been hindered by its cumbersome and time-consuming acquisition requirement of measuring susceptibility induced MR phase changes at multiple head orientations. Usually, sampling at more than six orientations is required to obtain sufficient information for the ill-posed STI dipole inversion. This complexity is enhanced by the limitation in head rotation angles due to physical constraints of the head coil. As a result, STI has not yet been widely applied in human studies in vivo. In this work, we tackle these issues by proposing an image reconstruction algorithm for STI that leverages data-driven priors. Our method, called DeepSTI, learns the data prior implicitly via a deep neural network that approximates the proximal operator of a regularizer function for STI. The dipole inversion problem is then solved iteratively using the learned proximal network. Experimental results using both simulation and in vivo human data demonstrate great improvement over state-of-the-art algorithms in terms of the reconstructed tensor image, principal eigenvector maps and tractography results, while allowing for tensor reconstruction with MR phase measured at much less than six different orientations. Notably, promising reconstruction results are achieved by our method from only one orientation in human in vivo, and we demonstrate a potential application of this technique for estimating lesion susceptibility anisotropy in patients with multiple sclerosis.

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, the scan time limitations may prohibit the acquisition of certain contrasts, and some contrasts may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts can improve diagnostic utility. For multi-contrast synthesis, the current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can, in turn, suffer from the loss of structural details in synthesized images. Here, in this paper, we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves intermediate-to-high frequency details via an adversarial loss, and it offers enhanced synthesis performance via pixel-wise and perceptual losses for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improve synthesis quality. Demonstrations on T1- and T2- weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to the previous state-of-the-art methods. Our synthesis approach can help improve the quality and versatility of the multi-contrast MRI exams without the need for prolonged or repeated examinations.