Heo, Jaehoon, et al. “PRIMO: A Full-Stack Processing-in-DRAM Emulation Framework for Machine Learning Workloads.” 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD). IEEE, 2023.
Abstract: The increasing size of deep learning models has made excessive memory access between AI processors and DRAM a major system bottleneck. Processing-in-DRAM (DRAM-PIM) offers a solution by integrating compute logic within memory, reducing external memory access. Existing simulators are often too slow for full applications, and FPGA-based emulators have been introduced, but none include the full software stack. This paper introduces PRIMO, the first full-stack DRAM-PIM emulation framework for end-to-end ML inference. PRIMO allows software developers to test ML workloads without real DRAM-PIM chips and helps designers explore design space. Our real-time FPGA emulator delivers results significantly faster than CPU-based simulations. We also provide a PIM compiler and driver to support various ML workloads with high bandwidth utilization. PRIMO achieves 106.64-6093.56× faster emulation for ML tasks compared to CPU simulations.
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