In autonomous driving, thermal image semantic segmentation has emerged as a critical research area, owing to its ability to provide robust scene understanding under adverse visual conditions. In particular, unsupervised domain adaptation (UDA) for thermal image segmentation can be an efficient solution to address the lack of labeled thermal datasets. Nevertheless, since these methods do not effectively utilize the complementary information between RGB and thermal images, they significantly decrease performance during domain adaptation. In this paper, we present a comprehensive study on cross-spectral UDA for thermal image semantic segmentation. We first propose a novel masked mutual learning strategy that promotes complementary information exchange by selectively transferring results between each spectral model while masking out uncertain regions. Additionally, we introduce a novel prototypical self-supervised loss designed to enhance the performance of the thermal segmentation model in nighttime scenarios. This approach addresses the limitations of RGB pre-trained networks, which cannot effectively transfer knowledge under low illumination due to the inherent constraints of RGB sensors. In experiments, our method achieves higher performance over previous UDA methods and comparable performance to state-of-the-art supervised methods.

Artificial neural networks have led to a higher computational burden, complicating inference tasks on low-power edge devices. Spiking neural network (SNN), which leverages sparse spikes for computation and data transmission, is an effective energy-efficient computing technique. However, the length of spike sequences in SNN varies significantly depending on the input coding method, among which rate coding still results in substantial data movement. A highly energy-efficient SNN accelerator with a time-domain CIM processor is proposed with three key features: 1) time-domain bitcell array for high linearity with lower energy, reducing 58.6% power consumption compared to inverter-chain architecture, 2) time-domain multi-bit accumulate for assisting multi-bit weights without analog-to-digital converter, achieving 47.2% energy reduction of domain-conversion energy, 3) analog precision reconstruction unit for supporting phase coding. The proposed TS-CIM is designed in 65 nm CMOS technology and achieves 701.7 TOPS/W energy efficiency, marking a$1.58\times $enhancement compared to the state-of-the-art SNN CIM.

This paper presents a content addressable memory (CAM)-based computing-in-memory (C$^{2}$IM) system designed for energy-efficient k-nearest neighbor (k-NN) searching in 3D point clouds. For autonomous driving applications, an essential process for perceiving the mobile robot’s movements in 3D space is k-NN searching. Especially with the limited hardware resources of mobile processors, the 3D point cloud is too large to upload onto the chip, leading to$O(N^{2})$of external memory accesses and distance calculations. The proposed C$^{2}$IM processor enhances energy efficiency and reduces power consumption through three key features: 1) Dilated 1D-CNN prediction enables voxel-based partitioning, reducing the external memory accesses from$O(N^{2})$to$O(N)$; 2) Vertex clustering reorganizes groups of points into evenly distributed clusters based on the underlying data distribution and reduces the number of points of comparisons by 49.8%; and 3) In-memory k-NN searching with CAM achieves high system energy efficiency while minimizing data transactions between memory and computation logic. Designed with 28 nm CMOS technology, the proposed C$^{2}$IM achieves up to 23.08$\times$energy efficiency, and 48.4% reduction in memory footprint compared to previous ASIC accelerators, and a 99.51% reduction in power consumption compared to state-of-the-art processor implemented in FPGA with high-bandwidth memory.

Intelligent 3D Interaction with Wide & Dynamic Surroundings