Does arithmetic logic dominate data movement? a systematic comparison of energy-efficiency for FFT accelerators

In this paper, we perform a systematic comparison to study the energy cost of varying data formats and data types w.r.t. arithmetic logic and data movement for accelerator-based heterogeneous systems in which both compute-intensive (FFT accelerator) and data-intensive accelerators (DLT accelerator) are added. We explore evaluation for a wide range of design processes (e.g. 32nm bulk-CMOS and projected 7nm FinFET) and memory systems (e.g. DDR3 and HMC). First, our result shows that when varying data formats, the energy costs of using floating point over fixed point are 5.3% (DDR3), 6.2% (HMC) for core and 0.8% (DDR3), 1.5% (HMC) for system in 32nm process. These energy costs are negligible as 0.2% and 0.01% for core and system in 7nm FinFET process in DDR3 memory and slightly increasing in HMC. Second, we identify that the core and system energy of systems using fixed point, 16-bit, FFT accelerator is nearly half of using 32-bit if data movement is also accelerated. This evidence implies that system energy is highly proportional to the amount of moving data when varying data types.