Reconfigurable dataflow graphs for processing-in-memory

Abstract

In order to meet the ever-increasing speed differences between processor clocks and memory access times, there has been an interest in moving computation closer to memory. The near data processing or processing-in-memory is particularly suited for very high bandwidth memories such as the 3D-DRAMs. There are different ideas proposed for PIMs, including simple in-order processors, GPUs, specialized ASICs and reconfigurable designs. In our case, we use Coarse-Grained Reconfigurable Logic to build dataflow graphs for computational kernels as the PIM. We show that our approach can achieve significant speedups and save energy consumed by computations. We evaluated our designs using several processing technologies for building the coarse-gained logic units. The DFPIM concept showed good performance improvement and excellent energy efficiency for the streaming benchmarks that were analyzed. The DFPIM in a 28 nm process with an implementation in each of 16 vaults of a 3D-DRAM logic layer showed an average speed-up of 7.2 over that using 32 cores of an Intel Xeon server system. The server processor required 368 times more energy to execute the benchmarks than the DFPIM implementation.