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DRAM-Less: Hardware Acceleration of Data Processing with New Memory

Jie Zhang, Gyuyoung Park, David Donofrio, John Shalf, Myoungsoo Jung

2020Year
4Citations
2Top-tier citations

Abstract

General purpose hardware accelerators become a major data processing resource in many computing domains. However, the processing capability of hardware accelerators is often limited by costly software overheads and memory copies to support compulsory data movement between different processors and solid-state drives. This in turn wastes a significant amount of energy in modern accelerated systems. In this work, we propose, DRAM-less, a hardware automation approach that integrates many state-of-the-art phase change memory (PRAM) modules into its data processing fabric to dramatically reduce the unnecessary data copies with a minimum of software modifications. We implement a new memory controller that plugs a real 3x nm multi-partition PRAM to 28nm FPGA logic cells and interoperate its design into a PCIe accelerator emulation platform. The evaluation results reveal that DRAM-less achieves, on average, 47% better performance than advanced acceleration approaches that use a peer-to-peer DMA (between a discrete accelerator and an SSD), while consuming only 19% of the total energy of such accelerated systems.

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