DOSAGE: Generating Domain-Specific Accelerators for Resource-Constrained Computing
Ankur Limaye, Tosiron Adegbija · 2021
Integrating low-overhead domain-specific accelerators with low-energy general-purpose processors can improve the processors’ performance efficiency in resource-constrained systems (e.g., embedded systems). However, current function-based approaches for designing domain-specific accelerators require substantial programmer efforts for hardware/software partitioning and program modifications to access the best available hardware accelerators. This paper presents DOSAGE, an LLVM compiler-based methodology to generate domain-specific accelerators for resource-constrained computing systems. Given a set of applications, DOSAGE automatically identifies and ranks the recurrent and similar code blocks that would benefit the most from hardware acceleration, based on the code blocks’ composition. We illustrate the benefits of the proposed approach using a case study that involves generating domain-specific accelerators for a diverse set of healthcare applications and evaluate the accelerators via FPGA-based prototyping. Compared to a base low-resource RISC-V processor, DOSAGE accelerators improved the system’s performance and energy by 24.85% and 8.54%, respectively. Furthermore, compared to a state-of-the-art function-based accelerator generation approach, DOSAGE eliminated the function-level granularity constraint of the generation process and reduced the number of required accelerators—and, in effect, the interfacing overhead—by 33.33%, while achieving equal or better program coverage and performance/energy results.