Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model

The increasing frequency and intensity of natural disasters demand more sophisticated approaches for rapid and precise damage assessment. To tackle this issue, researchers have developed various methods on disaster benchmark datasets from satellite imagery to aid in detecting disaster damage. However, the diverse nature of geographical landscapes and disasters makes it challenging to apply existing methods to regions unseen during training. We present DAVI (Disaster Assessment with VIsion foundation model), which overcomes domain disparities and detects structural damage (e.g., building) without requiring ground-truth labels of the target region. DAVI integrates task-specific knowledge from a model trained on source regions with an image segmentation foundation model to generate pseudo labels of possible damage in the target region. It then employs a two-stage refinement process, targeting both the pixel and overall image, to more accurately pinpoint changes in disaster-struck areas based on before-and-after images. Comprehensive evaluations demonstrate that DAVI achieves exceptional performance across diverse terrains (e.g., USA and Mexico) and disaster types (e.g., wildfires, hurricanes, and earthquakes). This confirms its robustness in assessing disaster impact without dependence on ground-truth labels.

A 4x32Gb/s 1.8pJ/bit Collaborative Baud-Rate CDR with Background Eye-Climbing Algorithm and Low-Power Global Clock Distribution

This paper presents design techniques for an energy-efficient multi-lane receiver (RX) with baud-rate clock and data recovery (CDR), which is essential for high-throughput low-latency communication in high-performance computing systems. The proposed low-power global clock distribution not only significantly reduces power consumption across multi-lane RXs but is capable of compensating for the frequency offset without any phase interpolators. To this end, a fractional divider controlled by CDR is placed close to the global phase locked loop. Moreover, in order to address the sub-optimal lock point of conventional baud-rate phase detectors, the proposed CDR employs a background eye-climbing algorithm, which optimizes the sampling phase and maximizes the vertical eye margin (VEM). Fabricated in a 28nm CMOS process, the proposed 4x32Gb/s RX shows a low integrated fractional spur of -40.4dBc at a 2500ppm frequency offset. Furthermore, it improves bit-error-rate performance by increasing the VEM by 17%. The entire RX achieves the energy efficiency of 1.8pJ/bit with the aggregate data rate of 128Gb/s.

A 0.65-pJ/bit 3.6-TB/s/mm I/O Interface with XTalk Minimizing Affine Signaling for Next-Generation HBM with High Interconnect Density

This paper presents an I/O interface with Xtalk Minimizing Affine Signaling (XMAS), which is designed to support high-speed data transmission in die-to-die communication over silicon interposers or similar high-density interconnects susceptible to crosstalk. The operating principles of XMAS are elucidated through rigorous analyses, and its advantages over existing signaling are validated through numerical experiments. XMAS not only demonstrates exceptional crosstalk removing capabilities but also exhibits robustness against noise, especially simultaneous switching noise. Fabricated in a 28-nm CMOS process, the prototype XMAS transceiver achieves an edge density of 3.6TB/s/mm and an energy efficiency of 0.65pJ/b. Compared to the single-ended signaling, the crosstalk-induced peak-to-peak jitter of the received eye with XMAS is reduced by 75% at 10GS/s/pin data rate, and the horizontal eye opening extends to 0.2UI at a bit error rate < 10$^{-12}$.