HRL: Efficient and Flexible Reconfigurable Logic for Near-Data Processing

The energy constraints due to the end of Dennard scaling, the popularity of in-memory analytics, and the advances in 3D integration technology have led to renewed interest in near-data processing (NDP) architectures that move processing closer to main memory. Due to the limited power and area budgets of the logic layer, the NDP compute units should be area and energy efficient while providing sufficient compute capability to match the high bandwidth of vertical memory channels. They should also be flexible to accommodate a wide range of applications. Towards this goal, NDP units based on fine-grained (FPGA) and coarse-grained (CGRA) reconfigurable logic have been proposed as a compromise between the efficiency of custom engines and the flexibility of programmable cores. Unfortunately, FPGAs incur significant area overheads for bit-level reconfiguration, while CGRAs consume significant power in the interconnect and are inefficient for irregular data layouts and control flows. This paper presents Heterogeneous Reconfigurable Logic (HRL), a reconfigurable array for NDP systems that improves on both FPGA and CGRA arrays. HRL combines both coarse-grained and fine-grained logic blocks, separates routing networks for data and control signals, and uses specialized units to effectively support branch operations and irregular data layouts in analytics workloads. HRL has the power efficiency of FPGA and the area efficiency of CGRA. It improves performance per Watt by 2.2x over FPGA and 1.7x over CGRA. For NDP systems running MapReduce, graph processing, and deep neural networks, HRL achieves 92% of the peak performance of an NDP system based on custom accelerators for each application.