Graph Convolutional Networks (GCNs) have been widely applied in fields such as social network analysis and recommendation systems. Recently, deep GCNs have emerged, enabling the exploration of deeper hidden information. Compared to traditional shallow GCNs, deep GCNs feature significantly more layers, leading to considerable computational and data movement challenges. Processing-In-Memory (PIM) offers a promising solution for efficiently handling GCNs by enabling near-data computation, thus reducing data transfer between processing units and memory. However, previous work mainly focused on shallow GCNs and has shown limited performance with deep GCNs. In this paper, we present Dancer, an innovative PIM-based GCN accelerator. Dancer optimizes data movement during the inference process, significantly improving efficiency and reducing energy consumption. Specifically, we introduce a novel compressed graph storage architecture and a dynamic quantization technique to minimize data transfers at each layer of the GCN. Additionally, through a detailed analysis of weight dynamics changes, we propose a sparsity propagation strategy to further alleviate the computational and data transfer burden between layers. Experimental results demonstrate that, compared to current state-of-the-art methods, Dancer achieves 3.7× speedup, 7.6× energy efficiency, and reduces of 9.6× DRAM access on average.

As the most common data structure for key-value stores, LogStructured Merge Tree (LSM-tree) can eliminate random write operations and keep acceptable read performance. However, write stall and write amplification introduced by the leveled compaction of LSM-tree significantly degrade the system performance. The emerging non-volatile memory (NVM) provides byte-addressable access and low-latency data persistence. Integrating DIMM-interface NVM in the design of the LSM-tree can potentially alleviate the write stall and write amplification issue, as the access speed of NVM is several orders of magnitude faster than hard disk drives or flash memory-based solid-state drives. This hybrid storage should be carefully designed, requiring new architectural and key-value structural support. This paper presents ZigZagDB, an NVM-enabled data man-agement scheme for LSM-tree-based key-value stores. ZigZagDB adds additional layers of key-value stores and uses non-volatile memory as the storage media to hold these additional layers of data. The newly designed key-value stores alternately access the data from either SSD or NVM. This ‘ZigZag’ shape of storage collaboration and synchronization can benefit write efficiency and space utilization. By utilizing the NVM with very limited capacity, the redesigned organization of LSM-tree can effectively solve the write stall and write amplification issue. We demonstrate the viability of the proposed ZigZagDB using a set of extensive experiments. Experimental results show that ZigZagDB can significantly reduce the write amplification and boost the throughput in comparison with representative schemes.