Function-Level Processor (FLP): A Novel Processor Class for Efficient Processing of Streaming Applications

The exponential growth in computation demand drives chip vendors to heterogeneous architectures combining Instruction-Level Processors (ILPs) and custom HW Accelerators (HWACCs) in an attempt to provide the needed processing capabilities while meeting power/energy requirements. ILPs, on one hand, are highly flexible, but power inefficient. Custom HWACCs, on the other hand, are inflexible (focusing on dedicated kernels), but highly power efficient. New processing architectures are needed that combine the power efficiency of HWACCs while still retaining sufficient flexibility to realize applications across targeted markets. This article introduces Function-Level Processors (FLPs) to fill the gap between ILPs and dedicated HWACCs. FLPs are comprised of configurable Function Blocks (FBs) implementing selected functions which are then interconnected via programmable point-to-point connections constructing an extensible/configurable macro data-path. An FLP raises programming abstraction to a Function-Set Architecture (FSA) controlling FBs allocation, configuration and scheduling. We demonstrate FLP benefits with an industry example of the Pipeline-Vision Processor (PVP). We highlight the gained flexibility by mapping 10 embedded vision applications entirely to the FLP-PVP offering up to 22.4 GOPs/s with an average power of 120 mW. The results also demonstrate that our FLP-PVP solution consumes 1/18th - 1/14th of the power of an ILP and 1/5th of the power of a hybrid ILP+HWACCs.