Enabling ILP-Based DSE for Multigranularity, Unified Domain Platforms With DmTSAR-ILP

Domain-specific HWACC-rich platforms blend high performance and efficiency presenting an opportunity to recover nonrecurring 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 (Zhang et al., 2023) 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 design space exploration (DSE), it cannot guarantee optimality. Exact solutions could be obtained with integer linear programming (ILP). Yet, the multiapplication DSE presents aggregation challenges as applications might have widely different performance, leading to nonlinear 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.1~\mathbf {mm}^{2}$ </tex-math></inline-formula> 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 mixed-integer programming solver performance.