MG-DmDSE: Multi-Granularity Domain Design Space Exploration Considering Function Similarity
Jinghan Zhang1,a, Aly Sultan1,b, Hamed Tabkhi2 and Gunar Schirner1,c
1Department of Electrical and Computer Engineering, Northeastern University, Boston, USA
azhangjinghan@ece.neu.edu
bsultan.a@northeastern.edu
cschirner@ece.neu.edu
2Department of Electrical and Computer Engineering, University of North Carolina Charlotte, USA
htabkhiv@uncc.edu
ABSTRACT
Heterogeneous accelerator-rich (ACC-rich) platforms combining general-purpose cores and specialized HW accelerators (ACCs) promise high-performance and low-power streaming application deployments in a variety of domains such as video analytics and software-defined radio. In order to benefit a domain of applications, a domain platform exploration tool must take advantage of structural and functional similarities across applications by allocating a common set of ACCs. A previous approach [1] proposed a GenetIc Domain Exploration tool (GIDE) that applied a restrictive binding algorithm that mapped applications functions to monolithic accelerators. This approach suffered from lower average application throughput across and reduced platform generality.
This paper introduces a Multi-Granularity based Domain Design Space Exploration tool (MG-DmDSE) to improve both average application throughput as well as platform generality. The key contributions of MG-DmDSE are: (1) Applying a multigranular decomposition of coarse grain application functions into more granular compute kernels. (2) Examining compute similarity between functions in order to produce more generic functions. (3) Configuring monolithic ACCs by selectively bypassing compute elements within them during DSE to expose more functionality. To assess MG-DmDSE, both GIDE and MGDmDSE were applied to applications in the OpenVX library. MGDmDSE achieves an average 2.84x greater application throughput compared to GIDE. Additionally, 87.5% of applications benefited from running on the platform produced by MG-DmDSE vs 50% from GIDE, which indicated increase platform generality.
