Adopting FPGA as an accelerator in datacenters is becoming mainstream for customized computing, but the fact that FPGAs are hard to program creates a steep learning curve for software programmers. Even with the help of high-level synthesis (HLS) , accelerator designers still have to manually perform code reconstruction and cumbersome parameter tuning to achieve optimal performance. While many learning models have been leveraged by existing work to automate the design of efficient accelerators, the unpredictability of modern HLS tools becomes a major obstacle for them to maintain high accuracy. To address this problem, we propose an automated DSE framework— AutoDSE —that leverages a bottleneck-guided coordinate optimizer to systematically find a better design point. AutoDSE detects the bottleneck of the design in each step and focuses on high-impact parameters to overcome it. The experimental results show that AutoDSE is able to identify the design point that achieves, on the geometric mean, 19.9× speedup over one CPU core for MachSuite and Rodinia benchmarks. Compared to the manually optimized HLS vision kernels in Xilinx Vitis libraries, AutoDSE can reduce their optimization pragmas by 26.38× while achieving similar performance. With less than one optimization pragma per design on average, we are making progress towards democratizing customizable computing by enabling software programmers to design efficient FPGA accelerators.
In software development environment, software companies usually ignore the user requirements validation process in requirement gathering phase, which results in large number of modifications being required in the software maintenance phase to fulfill the customer requirements. Identification of accurate requirements from user stories and determining the effectiveness of work deliverable of software industry has always been a challenging task. In this paper, a new measurement approach for forward engineering completeness for software was introduced by using requirements validation framework. The forward engineering completeness for software was measured in two steps. In the first step, software component structure was developed in order to find the functional and nonfunctional requirements rejected by the customers in the requirement validation framework. In the second step, completeness of software from component-based development was determined in which the following parameters, such as functional, non-functional completeness attributes, were considered in the measurement process, and the unadopted attributes of the reuse code were also considered. Quality level for the attributes were assigned based upon the valuation of interior quality of the source code. Therefore, it resulted in the reduction of development time required for the software and the cost required for the software development was also reduced. A case study was incorporated in this research to explain the measurement process of forward engineering completeness. If the forward engineering code is satisfying the quality standards, then the code is in the completeness form. The attributes of code that negates to be used were considered as unadopted attributes.
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