We introduce a new fault model for recent and future DRAM systems that uses empirical analysis to derive DRAM internal-component level fault models. This modeling level offers higher fidelity and greater predictive capability than prior models that rely on logical-address based characterization and modeling. We show how to derive the model, overcoming several challenges of using a publicly-available dataset of memory error logs. We then demonstrate the utility of our model by scaling it and analyzing the expected reliability of DDR5, HBM3, and LPDDR5 based systems. In addition to the novelty of the analysis and the model itself, we draw several insights regarding on-die ECC design and tradeoffs and the efficacy of repair/retirement mechanisms.

Matrix-matrix multiplication operations (GEMMs) are important in many HPC and machine-learning applications. They are often mapped to discrete accelerators (e.g., GPUs) to improve performance. However, we find that large tall/skinny and fat/short matrices benefit little from discrete acceleration and also do not perform well on a CPU. Such matrices are prevalent in important workloads, such as deep-learning inference within large-scale datacenters. We demonstrate the large potential of accelerating these GEMMs with processing in the main CPU memory, where processing in memory units (PIMs) take advantage of otherwise untapped bandwidth without requiring data copies. We develop a novel GEMM execution flow and corresponding memory-side address-generation logic that exploits GEMM locality and enables long-running PIM kernels despite the complex address-mapping functions employed by the CPU. Our evaluation of StepStone variants at the channel, device, and within-device PIM levels demonstrate 12X better minimum latency than a CPU and 2.8X greater throughput for strict query latency constraints. End-to-end performance analysis of recent recommendation and language models shows that StepStone outperforms a fast CPU by up to 16X and also the best prior main-memory acceleration approaches by up to 2.4X.