Transparent acceleration of data dependent instructions for general purpose processors

Although transistor scaling keeps following Moore’s law, and more area is available for designers, the clock frequency and ILP rate do not present the same level of growth anymore. This way, new architectural alternatives are necessary. Reconfigurable fabric appears to be one emerging possibility: besides exploiting the parallelism among instructions, it can also accelerate sequences of data dependent ones. However, coarse grain reconfiguration wide spread usage is still withhold by the need of special tools and compilers, which clearly do not sustain the reuse of legacy code without any modification. Based on all these facts, this work proposes a new Binary Translation algorithm, implemented in hardware and working in parallel to the processor, responsible for transforming sequences of instructions at run-time to be executed on a dynamic coarse-grain reconfigurable array, tightly coupled to a traditional RISC machine. Therefore, we can take advantage of using pure combinational logic to optimize even control-flow oriented code in a totally transparent process, without any modification in the source or binary codes. Using the Simplescalar Toolset together with the embedded benchmark suite MIBench, we show performance improvements and area evaluation when comparing against traditional superscalar architectures.