Spatz: Clustering Compact RISC-V-Based Vector Units to Maximize Computing Efficiency

The ever-increasing computational and storage requirements of modern applications and the slowdown of technology scaling pose major challenges to designing and implementing efficient computer architectures. In this paper, we leverage the architectural balance principle to alleviate the bandwidth bottleneck at the L1 data memory boundary of a tightly-coupled cluster of processing elements (PEs). We thus explore coupling each PE with an L0 memory, namely a private register file implemented as Standard Cell Memory (SCM). Architecturally, the SCM is the Vector Register File (VRF) of Spatz, a compact 64-bit floating-point-capable vector processor based on RISC-V's Vector Extension Zve64d. Unlike typical vector processors, whose VRF are hundreds of KiB large, we prove that Spatz can achieve peak energy efficiency with a VRF of only 2 KiB. An implementation of the Spatz-based cluster in GlobalFoundries' 12LPP process with eight double-precision Floating Point Units (FPUs) achieves an FPU utilization just 3.4% lower than the ideal upper bound on a double-precision, floating-point matrix multiplication. The cluster reaches 7.7 FMA/cycle, corresponding to 15.7 GFLOPS-DP and 95.7 GFLOPS-DP/W at 1 GHz and nominal operating conditions (TT, 0.80V, 25^oC) with more than 55% of the power spent on the FPUs. Furthermore, the optimally-balanced Spatz-based cluster reaches a 95.0% FPU utilization (7.6 FMA/cycle), 15.2 GFLOPS-DP, and 99.3 GFLOPS-DP/W (61% of the power spent in the FPU) on a 2D workload with a 7x7 kernel, resulting in an outstanding area/energy efficiency of 171 GFLOPS-DP/W/mm^2. At equi-area, our computing cluster built upon compact vector processors reaches a 30% higher energy efficiency than a cluster with the same FPU count built upon scalar cores specialized for stream-based floating-point computation.