Donghyuk Lee

Sangkug Lym, Donghyuk Lee, Mike O'Connor et al.

Training convolutional neural networks (CNNs) requires intense compute throughput and high memory bandwidth. Especially, convolution layers account for the majority of the execution time of CNN training, and GPUs are commonly used to accelerate these layer workloads. GPU design optimization for efficient CNN training acceleration requires the accurate modeling of how their performance improves when computing and memory resources are increased. We present DeLTA, the first analytical model that accurately estimates the traffic at each GPU memory hierarchy level, while accounting for the complex reuse patterns of a parallel convolution algorithm. We demonstrate that our model is both accurate and robust for different CNNs and GPU architectures. We then show how this model can be used to carefully balance the scaling of different GPU resources for efficient CNN performance improvement.

Yoongu Kim, Ross Daly, Jeremie Kim et al.

As process technology scales down to smaller dimensions, DRAM chips become more vulnerable to disturbance, a phenomenon in which different DRAM cells interfere with each other's operation. For the first time in academic literature, our ISCA paper exposes the existence of disturbance errors in commodity DRAM chips that are sold and used today. We show that repeatedly reading from the same address could corrupt data in nearby addresses. More specifically: When a DRAM row is opened (i.e., activated) and closed (i.e., precharged) repeatedly (i.e., hammered), it can induce disturbance errors in adjacent DRAM rows. This failure mode is popularly called RowHammer. We tested 129 DRAM modules manufactured within the past six years (2008-2014) and found 110 of them to exhibit RowHammer disturbance errors, the earliest of which dates back to 2010. In particular, all modules from the past two years (2012-2013) were vulnerable, which implies that the errors are a recent phenomenon affecting more advanced generations of process technology. Importantly, disturbance errors pose an easily-exploitable security threat since they are a breach of memory protection, wherein accesses to one page (mapped to one row) modifies the data stored in another page (mapped to an adjacent row).