Cache Aware Scheduling for Synchronous Dataflow Programs

The Synchronous Dataflow (SDF) model of computation [1] is an efficient and popular way to represent signal processing systems. In an SDF model, the amount of data produced and consumed by a data flow actor is specified a priori for each input and output. SDF specifications allow static generation of highly optimized schedules, which may be optimized according to one or more criteria, such as minimum buffer size, maximum throughput, maximum processor utilization, or minimum program memory. In this report, we analyze the effect of cache architecture on the execution time of an SDF schedule and develop a new heuristic approach to generating SDF schedules with reduced execution time for a particular cache architecture. In this report, we consider the implementation of well-ordered SDF graphs on a single embedded Digital Signal Processor (DSP). We assume a simple Harvard memory architecture DSP with single-level caches and separate instruction and data-memory. In order to predict execution times, we propose a cache management policy for the data cache and argue that this policy outperforms traditional cache policies when executing SDF models. We also replace the instruction cache by a scratchpad memory with software-controlled replacement policy. Using our data cache and instruction scratchpad policies, we show that different schedules can have vastly different execution times for a given set of data cache and instruction scratchpad sizes. In addition, we show that existing scheduling techniques often create schedules that perform poorly with respect to cache usage. 3 In order to improve cache performance, an optimal cache-aware scheduler would minimize the total cache miss penalty by simultaneously considering both data and instruction miss penalties. Unfortunately, reducing data cache misses often increases instruction scratchpad misses and vice versa. In this report, we show that the number of schedules that must be considered increases exponentially according to the vectorization factor of the schedule. To address this complexity, we develop an SDF scheduling algorithm based on a greedy, cache-aware heuristic. We compare the resulting schedules with schedules generated by existing SDF scheduling schemes. The schedule generated by our algorithm poses an interesting problem of code generation. We also propose a solution to address this problem. This work is highly applicable in the design of SDF systems that are implemented as Systems on Chip (SoC) with DSP cores.