Enabling Efficient Hybrid Systolic Computation in Shared L1-Memory Manycore Clusters
<|reference_start|>Enabling Efficient Hybrid Systolic Computation in Shared L1-Memory Manycore Clusters: Systolic arrays and shared-L1-memory manycore clusters are commonly used architectural paradigms that offer different trade-offs to accelerate parallel workloads. While the first excel with regular dataflow at the cost of rigid architectures and complex programming models, the second are versatile and easy to program but require explicit dataflow management and synchronization. This work aims at enabling efficient systolic execution on shared-L1-memory manycore clusters. We devise a flexible architecture where small and energy-efficient RISC-V cores act as the systolic array's processing elements (PEs) and can form diverse, reconfigurable systolic topologies through queues mapped in the cluster's shared memory. We introduce two low-overhead RISC-V ISA extensions for efficient systolic execution, namely Xqueue and Queue-linked registers (QLRs), which support queue management in hardware. The Xqueue extension enables single-instruction access to shared-memory-mapped queues, while QLRs allow implicit and autonomous access to them, relieving the cores of explicit communication instructions. We demonstrate Xqueue and QLRs in MemPool, an open-source shared-memory cluster with 256 PEs, and analyze the hybrid systolic-shared-memory architecture's trade-offs on several DSP kernels with diverse arithmetic intensity. For an area increase of just 6%, our hybrid architecture can double MemPool's compute unit utilization, reaching up to 73%. In typical conditions (TT/0.80V/25{\deg}C), in a 22 nm FDX technology, our hybrid architecture runs at 600 MHz with no frequency degradation and is up to 65% more energy efficient than the shared-memory baseline, achieving up to 208 GOPS/W, with up to 63% of power spent in the PEs.<|reference_end|>
@article{mazzola2024enabling,
    title={Enabling Efficient Hybrid Systolic Computation in Shared L1-Memory
  Manycore Clusters},
    author={Sergio Mazzola, Samuel Riedel, Luca Benini},
    journal={arXiv preprint arXiv:2402.12986},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12986},
    primaryClass={cs.AR}
}