  Graph convolutional network (GCN), an emerging algorithm for graph computing,
has achieved promising performance in graphstructure tasks. To achieve
acceleration for data-intensive and sparse graph computing, ASICs such as GCNAX
have been proposed for efficient execution of aggregation and combination in
GCN. GCNAX reducing 8x DRAM accesses compared with previous efforts. However,
as graphs have reached terabytes in size, off-chip data movement from SSD to
DRAM becomes a serious latency bottleneck. This paper proposes Compressive
Graph Transmission (CGTrans), which performs the aggregation in SSD to
dramatically relieves the transfer latency bottleneck due to SSD loading
compared to CMOS-based graph accelerator ASICs. InSSD computing technique is
required for CGTrans. Recently, Insider was proposed as a near-SSD processing
system computing by integrating FPGA in SSD. However, the Insider still suffers
low area efficiency, which will limit the performance of CGTrans. The recently
proposed Fully Concurrent Access Technique (FAST) is utilized. FAST-GAS, as an
in-SSD graph computing accelerator, is proposed to provide high-concurrent
gather-andscatter operations to overcome the area efficiency problem. We
proposed the GRAPHIC system containing CGTrans dataflow deployed on FAST-GAS.
Experiments show CGTrans reduces SSD loading by a factor of 50x, while GRAPHIC
achieves 3.6x, and 2.4x speedup on average over GCNAX and CGTrans on Insider,
respectively.
