Designing error correction code (ECC) to guarantee strong reliability for high bandwidth memory (HBM) is imperative in high performance computers, especially for systems equipped with graphics processing units (GPUs). The design of ECC is challenging because future GPUs are expected to implement a memory subsystem supporting fine and coarse-grained data accesses to match the difference in the spatial locality of GPGPU applications. Current ECC designs, however, are developed for a fixed data fetch granularity. To have a more flexible design, we propose a novel memory protection scheme, called Config(urable)-ECC, which provides strong reliability for both fine and coarse-grained data accesses. Config-ECC consists of two tiers of ECC protection. The tier-1 code is a strong product code that can correct errors due to small granularity faults and detect errors caused by large granularity faults. The tier-2 code is an XOR-based code that is employed to correct errors incurred by large granularity faults. Config-ECC provides stronger reliability and/or lower energy consumption compared to state-of-the-art fixed 32B and 64B ECC schemes. It reduces the HBM energy by 17-21 percent while reducing the failure in time (FIT) rate by 20 times compared to a state-of-the-art fixed 64B ECC scheme with an insignificant 1.2 percent performance overhead.

This article proposes a rate-adaptive, two-tiered error-correction scheme (RATT-ECC) that provides strong reliability (10 10 x reduction in raw FIT rate) for an HBM-like 3D DRAM system. The tier-1 code is a strong symbol-based code that can correct errors due to small granularity faults and detect errors caused by large granularity faults; the tier-2 code is an XOR-based code that corrects errors detected by the tier-1 code. The rate-adaptive feature of RATT-ECC enables permanent bank failures to be handled through sparing. It can also be used to significantly reduce the refresh power consumption without decreasing reliability and timing performance.

Most server-grade systems provide Chipkill-Correct error protection at the expense of power and performance. In this paper we present a low overhead solution to improving the reliability of commodity DRAM systems with no change in the existing memory architecture. Specifically, we propose five erasure and error correction (E-ECC) schemes that provide at least Chipkill-Correct protection for x4 (Schemes 1, 2 and 3), x8 (Scheme 4) and x16 (Scheme 5) DRAM systems. All schemes have superior error correction performance due to the use of strong symbol-based codes. Synthesis results in 28 nm node show that the decoding latency of these codes is negligible compared to the DRAM access latency. In addition, we make use of erasure codes to extend the lifetime of the DRAM systems. Specifically, once a chip is marked faulty due to persistent errors, all E-ECC schemes correct erasures due to that faulty chip and also correct an additional random error in a second chip. Evaluation with SPEC2006 workloads show that compared to x4 Chipkill-Correct schemes, Scheme 5 has the highest IPC improvement (mean of 7 percent) and Scheme 4 has the largest power reduction (mean of 18 percent) and the largest increase in energy efficiency (mean of 25 percent).

The power target for exascale supercomputing is 20MW, with about 30% budgeted for the memory subsystem. Commodity DRAMs will not satisfy this requirement. Additionally, the large number of memory chips (>10M) required will result in crippling failure rates. Although specialized DRAM memories have been reorganized to reduce power through 3D-stacking or row buffer resizing, their implications on fault tolerance have not been considered. We show that addressing reliability and energy is a co-optimization problem involving tradeoffs between error correction cost, access energy and refresh power---reducing the physical page size to decrease access energy increases the energy/area overhead of error resilience. Additionally, power can be reduced by optimizing bitline lengths. The proposed 3D-stacked memory uses a page size of 4kb and consumes 5.1pJ/bit based on simulations with NEK5000 benchmarks. Scaling to 100PB, the memory consumes 4.7MW at 100PB/s which, while well within the total power budget (20MW), is also error-resilient.