Flexagon: A Multi-Dataflow Sparse-Sparse Matrix Multiplication Accelerator for Efficient DNN Processing
Sparsity is a growing trend in modern DNN models. Existing Sparse-Sparse Matrix Multiplication (SpMSpM) accelerators are tailored to a particular SpMSpM dataflow (i.e., Inner Product, Outer Product or Gustavsons), that determines their overall efficiency. We demonstrate that this static decision inh...
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Zusammenfassung: | Sparsity is a growing trend in modern DNN models. Existing Sparse-Sparse
Matrix Multiplication (SpMSpM) accelerators are tailored to a particular SpMSpM
dataflow (i.e., Inner Product, Outer Product or Gustavsons), that determines
their overall efficiency. We demonstrate that this static decision inherently
results in a suboptimal dynamic solution. This is because different SpMSpM
kernels show varying features (i.e., dimensions, sparsity pattern, sparsity
degree), which makes each dataflow better suited to different data sets. In
this work we present Flexagon, the first SpMSpM reconfigurable accelerator that
is capable of performing SpMSpM computation by using the particular dataflow
that best matches each case. Flexagon accelerator is based on a novel
Merger-Reduction Network (MRN) that unifies the concept of reducing and merging
in the same substrate, increasing efficiency. Additionally, Flexagon also
includes a 3-tier memory hierarchy, specifically tailored to the different
access characteristics of the input and output compressed matrices. Using
detailed cycle-level simulation of contemporary DNN models from a variety of
application domains, we show that Flexagon achieves average performance
benefits of 4.59x, 1.71x, and 1.35x with respect to the state-of-the-art
SIGMA-like, Sparch-like and GAMMA-like accelerators (265% , 67% and 18%,
respectively, in terms of average performance/area efficiency). |
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DOI: | 10.48550/arxiv.2301.10852 |