TransRec: Improving Adaptability in Single-ISA Heterogeneous Systems with Transparent and Reconfigurable Acceleration
Marcelo Brandalero1,a, Muhammad Shafique2, Luigi Carro1,b and Antonio Carlos Schneider Beck1,c
1Institute of Informatics, Universidade Federal do Rio Grande do Sul (UFRGS). Porto Alegre, Brazil
ambrandalero@inf.ufrgs.br
bcarro@inf.ufrgs.br
ccaco@inf.ufrgs.br
2Institute of Computer Engineering, Vienna University of Technology (TU Wien). Vienna, Austria
muhammad.shafique@tuwien.ac.at
ABSTRACT
Single-ISA heterogeneous systems, such as ARM’s big.LITTLE, use microarchitecturally-different General-Purpose Processor cores to efficiently match the capabilities of the processing resources with applications’ performance and energy requirements that change at run time. However, since only a fixed and non-configurable set of cores is available, reaching the bestpossible match between the available resources and applications’ requirements remains a challenge, especially considering the varying and unpredictable workloads. In this work, we propose TransRec, a hardware architecture which improves over these traditional heterogeneous designs. TransRec integrates a shared, transparent (i.e., no need to change application binary) and adaptive accelerator in the form of a Coarse-Grained Reconfigurable Array that can be used by any of the General-Purpose Processor cores for on-demand acceleration. Through evaluations with cycle-accurate gem5 simulations, synthesis of real RISC-V processor designs for a 15nm technology, and considering the effects of Dynamic Voltage and Frequency Scaling, we demonstrate that TransRec provides better performance-energy tradeoffs that are otherwise unachievable with traditional big.LITTLE-like designs. In particular, for less than 40% area overhead, TransRec can improve performance in the low-energy mode (LITTLE) by 2.28×, and can improve both performance and energy efficiency by 1.32× and 1.59×, respectively, in high-performance mode (big).
Keywords: Adaptive systems, Reconfigurable systems, Big.LITTLE, Accelerators, Flexibility, Performance, Energy, Efficiency.
