Generating high-quality depth maps with accurate values is a critical research topic, and various depth estimation methods, such as Time-of-Flight (ToF) and Monocular Depth Estimation (MDE), are advancing rapidly. However, each single-sensor approach is inherently limited by the characteristics of its respective modality. Meanwhile, fused approaches using multiple sensors or dedicated trained models are often plagued by system complexity and limited generalization. In this paper, we propose an adaptive depth processing system based on the foundation transformer and ToF fusion, aiming to harness the precision of ToF data and the high-quality depth priors provided by the foundation model in a monocular and zero-shot manner. A hardware–algorithm co-design partitions the computation between a dedicated ASIC for compute-intensive foundation transformer acceleration and an MPSoC FPGA for potential reconfigurable fusion, yielding 36.4 ms latency and 1.9 W power under a 27.6 GOPS/frame workload. Zero-shot evaluations on various ToF data sources including FLAT, TICaM, and in-house captures confirm strong cross-domain generalization, demonstrating the system’s adaptability, efficiency, and accuracy.

Ensuring the confidentiality and integrity of DNN accelerators is paramount across various scenarios spanning autonomous driving, healthcare, and finance. However, current security approaches typically require extensive hardware resources, and incur significant off-chip memory access overheads. This paper introduces SeDA, which utilizes 1) a bandwidth-aware encryption mechanism to improve hardware resource efficiency, 2) optimal block granularity through intra-layer and inter-layer tiling patterns, and 3) a multi-level integrity verification mechanism that minimizes, or even eliminates, memory access overheads. Experimental results show that SeDA decreases performance overhead by over 12% for both server and edge neural processing units (NPUs), while ensuring robust scalability.11SeDA source code:https://github.com/wayne4s/seda.git

Biologically plausible Spiking Neural Networks (SNNs), characterized by spike sparsity, are growing tremendous attention over intellectual edge devices and critical bio-medical applications as compared to artificial neural networks (ANNs). However, there is a considerable risk from malicious attempts to extract white-box information (i.e., weights) from SNNs, as attackers could exploit well-trained SNNs for profit and white-box adversarial concerns. There is a dire need for intellectual property (IP) protective measures. In this paper, we present a novel secure software-hardware co-designed RRAM-based neuromorphic accelerator for protecting the IP of SNNs. Software-wise, we design a tailored genetic algorithm with classic XOR encryption to target the least number of weights that need encryption. From a hardware perspective, we develop a low-energy decryption module, meticulously designed to provide zero decryption latency. Extensive results from various datasets, including NMNIST, DVSGesture, EEGMMIDB, Braille Letter, and SHD, demonstrate that our proposed method effectively secures SNNs by encrypting a minimal fraction of stealthy weights, only 0.00005% to 0.016% weight bits. Additionally, it achieves a substantial reduction in energy consumption, ranging from ×59 to ×6780, and significantly lowers decryption latency, ranging from ×175 to ×4250. Moreover, our method requires as little as one sample per class in dataset for encryption and addresses hessian/gradient-based search insensitive problems. This strategy offers a highly efficient and flexible solution for securing SNNs in diverse applications1.

Indirect time-of-flight (iToF) is one of the most straightforward approaches to capture 3D images. However, due to the nature of the iToF camera, it is still challenging to get reliable and accurate depth images from the raw data in the image signal processor (ISP) pipeline due to environmental issues. Previous iToF ISP works mainly focus on the traditional pipeline, such as filters and depth calculation. In this work, we present an end-to-end deep-learning-based iToF ISP. The proposed iToF ISP system can generate real-time depth images with deep-learning-based noise reduction and multipath interference (MPI) reduction. With the mixed-bit convolutional neural network (CNN) with 96.5 % sparsity and the mixed-bit sparse accelerator, the CNN is accelerated by 2.78× and negligible mean average error (MAE) loss has been achieved on the FLAT dataset using the proposed ISP pipeline.

Deep neural networks (DNNs) usually have multiple layers and thousands of trainable parameters to ensure high accuracy. Due to the requirement of large amounts of computation and memory, these networks are not suitable for real-time and resource-constrained mobile or embedded systems. Various techniques such as network pruning, weight sharing, network quantization, and weight encoding have been proposed to improve computational and memory efficiency. This paper presents a synchronous weight quantization-compression (SWQC) technique to compress the weights of low-bit quantized neural network (QNN). Specifically, it quantizes the weights not strictly according to their values but based on compression efficiency and their probabilities of being different quantized results. In the process of weight quantization, the compression efficiency of weights is considered as an important factor. With the help of retraining, a high compression rate and accuracy can be achieved. Verification is performed on 4-bit QNNs using the MNIST and CIFAR10 datasets. Results show that no classification accuracy is lost when the compression rate approaches 5.4X and 4.4X for the two datasets, respectively. The compression rate of the MNIST experiment is increased to 12.1X with a 1% accuracy drop, while the CIFAR10 experiment achieves a compression rate of 5.6X with the accuracy drop of about 0.6%.

OFDM is facing great opportunities and challenges in current broadband communication era. These opportunities and challenges derive from the native advantages and disadvantages of OFDM technology respectively. Too high PAPR is one of the main problems that prevent OFDM from being used more generally in broadband systems. Many approaches such as clipping and filtering, coding, SLM, PTS, and tone reservation have been studied to reduce the peak magnitude of OFDM symbols. In these approaches, tone reservation is considered as one of the most promising methods because of no additional distortion, no side information, and low implementation cost. In this paper, a novel tone reservation scheme is presented. Its essential idea is that a subcarrier selected from all reserved subcarriers for PAPR reduction should have a phase close to one of phi, pi/2+phi, pi+phi and -pi/2+phi, at the peak location in time domain, where phi is the phase of the peak sample. This results in no complex multiplication and division in the novel scheme. In addition, the floating positions of such selected subcarriers in frequency domain are helpful to the convergence of the algorithm. The simulation results show that the scheme can provide good performance and fast convergence.