Early DSE and Automatic Generation of Coarse-grained Merged Accelerators

Post-Moore's law area-constrained systems rely on accelerators to deliver performance enhancements. Coarse-grained accelerators can offer substantial domain acceleration, but manual, ad hoc identification of code to accelerate is prohibitively expensive. Because cycle-accurate simulators and high-level synthesis (HLS) flows are so time-consuming, the manual creation of high-utilization accelerators that exploit control and data flow patterns at optimal granularities is rarely successful. To address these challenges, we present AccelMerger, the first automated methodology to create coarse-grained, control- and data-flow-rich merged accelerators. AccelMerger uses sequence alignment matching to recognize similar function call-graphs and loops, and neural networks to quickly evaluate their post-HLS characteristics. It accurately identifies which functions to accelerate, and it merges accelerators to respect an area budget and to accommodate system communication characteristics like latency and bandwidth. Merging two accelerators can save as much as 99% of the area of one. The space saved is used by a globally optimal integer linear program to allocate more accelerators for increased performance. We demonstrate AccelMerger's effectiveness using HLS flows without any manual effort to fine-tune the resulting designs. On FPGA-based systems, AccelMerger yields application performance improvements of up to 16.7x over software implementations, and 1.91x on average with respect to state-of-the-art early-stage design space exploration tools.

Automated summary

Post-Moore's law systems rely on accelerators for performance enhancements. AccelMerger is an automated methodology that creates coarse-grained, merged accelerators with control and data flow. It uses sequence alignment matching and neural networks to identify functions to accelerate and merge accelerators within an area budget.