DmTSAR-ILP: Allocating a Unified Domain Platform for Streaming Applications
Bruno Morais, Gunar Schirner · 2023
Domain-specific HWACC-rich platforms blend high performance & efficiency presenting an opportunity to recover non-recurring engineering costs through wider deployment for many applications. However, the design of such platforms is immensely challenging, partly due to the design space size.MG-DmDSE [1] allocates domain platforms catering to multiple applications. To cope with complexity, it employs a genetic algorithm and heuristics. While this is a typical approach in DSE, it cannot guarantee optimality. Exact solutions could be obtained with integer linear programming (ILP). Yet, the multi-application DSE presents aggregation challenges as applications might have widely different performance, leading to non-linear aggregation techniques in previous work that ILPs cannot employ.This work introduces DmTSAR-ILP, an ILP-based platform allocation method that simultaneously considers all applications in a domain. DmTSAR-ILP captures the same underlying analytical model as MG-DmDSE and can explore its domain application set. To enable our linear domain-level formulation, we introduce the average performance achievement metric (APA), offering an ILP-friendly, fair aggregation across domain applications.To highlight benefits of DmTSAR-ILP, a domain platform for 40 OpenVX apps is generated (across varying area budgets). For all area budgets, DmTSAR-ILP platforms perform better or identical to MG-DmDSE even in their original metric. The proposed APA aggregation is fair, allowing more applications (55% vs 37.5% of applications in MG-DmDSE) to be fully accelerated on the generated platform. For the 0.1 mm 2 area budget, DmTSAR-ILP’s platform increases application throughput by 22.5% over MG-DmDSE’s platform. DmTSAR-ILP is 70x faster on average than the heuristic-based MG-DmDSE by leveraging an ILP formulation that considers all applications simultaneously and recent advances in MIP solver performance.