Soft processing engines make FPGA programming simpler for software programmers. TILT is a multithreaded soft processing engine that contains multiple deeply pipelined and varying latency functional units. In this thesis, we present a compiler framework for compiling and scheduling for TILT. By using the compiler to generate schedules and manage hardware we create computationally dense designs (high throughput per hardware area) which make compelling processing engines. High schedule density is achieved by mixing instructions from different threads and by prioritizing the longest path of data flow graphs. Averaged across benchmark kernels we can achieve 90% of the theoretical throughput, and can reduce the performance gap relative to custom hardware from 543x for a scalar processor to only 4.41x by replicating TILT cores up to a comparable area cost. We also present methods of quickly navigating the design space and predicting the area of hardware configurations.

Soft processing engines make FPGA programming simpler for software programmers. TILT is a multithreaded soft processing engine that contains multiple deeply pipelined and varying latency functional units. In this thesis, we present a compiler framework for compiling and scheduling for TILT. By using the compiler to generate schedules and manage hardware we create computationally dense designs (high throughput per hardware area) which make compelling processing engines. High schedule density is achieved by mixing instructions from different threads and by prioritizing the longest path of data flow graphs. Averaged across benchmark kernels we can achieve 90% of the theoretical throughput, and can reduce the performance gap relative to custom hardware from 543x for a scalar processor to only 4.41x by replicating TILT cores up to a comparable area cost. We also present methods of quickly navigating the design space and predicting the area of hardware configurations.