  We propose a dense tensor accelerator called VectorMesh, a scalable,
memory-efficient architecture that can support a wide variety of DNN and
computer vision workloads. Its building block is a tile execution unit~(TEU),
which includes dozens of processing elements~(PEs) and SRAM buffers connected
through a butterfly network. A mesh of FIFOs between the TEUs facilitates data
exchange between tiles and promote local data to global visibility. Our design
performs better according to the roofline model for CNN, GEMM, and spatial
matching algorithms compared to state-of-the-art architectures. It can reduce
global buffer and DRAM fetches by 2-22 times and up to 5 times, respectively.
