Presentation
ADEM: Accelerating Sparse Matrix Multiplication with Adaptive Dataflow and Efficient Merging
DescriptionSparse matrix multiplication (SpMSpM) is a critical computational kernel in various scientific and machine learning applications. The diverse sparsity patterns of different SpMSpM workloads present a significant challenge for traditional fixed-dataflow accelerators. Recent approaches have attempted to leverage dynamic dataflow to capture varying memory access characteristics under different sparsity patterns; however, they still face challenges in maximizing data reuse, load balance, and efficient merging of patial sums. To address these issues, we propose ADEM, an adaptive dataflow-based SpMSpM accelerator that can dynamically adjust the matrix partitioning scheme at a fine-grained level. Additionally, ADEM incorporates a greedy-based scheduling algorithm to achieve load balancing. Finally, we employ heterogeneous merge units to handle two distinct types of merging tasks. Experimental results demonstrate that ADEM achieves average performance gains of 8.1x and 1.88x over the baseline.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby