Energy aware synthesis of application kernels through composition of data-paths on a CGRA

Abstract

Transistor supply voltages no longer scales at the same rate as transistor density and frequency of operation. This has led to the Dark Silicon problem, wherein only a fraction of transistors can operate at maximum frequency and nominal voltage, in order to ensure that the chip functions within the power and thermal budgets. Heterogeneous computing systems which consist of General Purpose Processors (GPPs), Graphic Processing Units (GPUs) and application specific accelerators can provide improved performance while keeping power dissipation at a realistic level. For the accelerators to be effective, they have to be specialized for related classes of application kernels and have to be synthesized from high level specifications. Coarse Grained Reconfigurable has been proposed as accelerators for a variety of application kernels. For CGRAs to be used as accelerators in the Dark Silicon era, a synthesis framework which focuses on optimizing energy efficiency, while achieving the target performance is essential. However, existing compilation techniques for CGRAs focuses on optimizing only for performance, and any reduction in energy is just a side-effect. In this paper we explore synthesizing application kernels expressed as functions, on a coarse grained composable reconfigurable array (CGCRA). The proposed reconfigurable array comprises HyperCells, which are reconfigurable macro-cells that facilitate modeling power and performance in terms of easily measurable parameters. The proposed synthesis approach takes kernels expressed in a functional language, applies a sequence of well known program transformations, explores trade-offs between throughput and energy using the power and performance models, and realizes the kernels on the CGCRA. This approach when used to map a set of signal processing and linear algebra kernels achieves resource utilization varying from 50% to 80%.