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Tensor decomposition has become an essential tool in many applications in various domains, including machine learning. Sparse Matricized Tensor Times Khatri-Rao Product (MTTKRP) is one of the most computationally expensive kernels in tensor computations. Despite having significant computational parallelism, MTTKRP is a challenging kernel to optimize due to its irregular memory access characteristics. This paper focuses on a multi-faceted memory system, which explores the spatial and temporal locality of the data structures of MTTKRP. Further, users can reconfigure our design depending on the behavior of the compute units used in the FPGA accelerator. Our system efficiently accesses all the MTTKRP data structures while reducing the total memory access time, using a distributed cache and Direct Memory Access (DMA) subsystem. Moreover, our work improves the memory access time by 3.5&#x00D7; compared with commercial memory controller IPs. Also, our system shows 2&#x00D7; and 1.26&#x00D7; speedups compared with cache-only and DMA-only memory systems, respectively.

Reconfigurable Low-latency Memory System for Sparse Matricized Tensor Times Khatri-Rao Product on FPGA