In database systems, cost estimation for query plans has a variety of uses, including query optimization, resource management, load balancing, query scheduling, performance monitoring, and automated maintenance. Existing methods mainly targeted B-tree-based systems, where costs and cardinality are highly correlated. However, LSM-Trees, due to their unique storage structure, violate the assumptions of existing learning methods, causing cardinality to be irrelevant to cost estimation. In addition, the constantly changing data layout leads to severe data drifts when updating data, preventing current learning based models from accurately estimating costs in an agile way. To address these challenges, we propose a dual-layer end-to end cost estimation model for LSM-Tree-based database systems. This model treats cost estimation as a regression problem and comprises two layers: the storage layer and the query plan layer. The storage layer employs lightweight neural networks to leverage data distribution, provide information to the query plan layer, and address the challenges posed by data drift in LSM Trees. The query plan layer uses the Transformer framework and incorporates structural information to learn the representation of plans. This dual-layer architecture allows our model to effectively embed storage information and query plan tree details. The results show that our proposed model achieves state-of-the art cost estimation accuracy for database systems based on LSM-Trees. Additionally, our architecture significantly reduces the model's updating costs, ensuring robust performance amid frequent data drifts.

Tightly integrated optical interconnects can provide high-bandwidth, energy-efficient inter-node communication. We describe a novel system which uses hybrid 2.5D/3D integration to compose a state-of-the-art FPGA compute chiplet, three electrical interface chiplets, and three photonic interface chiplets. We use register-transfer-, gate-, transistor-, and device-level simulations to demonstrate the potential for this system to achieve 96Tb/s of bi-directional bandwidth, and we experimentally demonstrate key components including a complete opto-electrical channel. Our results provide a strong case for hybrid 2.5D/3D integration as the key enabler for scaling co-packaged optical interconnects.

We demonstrate a dense, highly parallel, and scalable multi-channel transceiver array for photonic chip-to-chip links. A CMOS electronic chip is flip-chipped onto a silicon photonic chip with 25 μm-pitch bumps to enable 0.8 Tbps data transmission through a single fiber utilizing a comb laser at a record bandwidth density of 5.3 Tbps/mm2. The unit transmitter and receiver cells inside the 80 channel-array incorporate high speed circuitry to drive, read from, and tune the photonic elements, while only occupying 25 μm × 75 μm each.