8 research outputs found
GraphR: Accelerating Graph Processing Using ReRAM
This paper presents GRAPHR, the first ReRAM-based graph processing
accelerator. GRAPHR follows the principle of near-data processing and explores
the opportunity of performing massive parallel analog operations with low
hardware and energy cost. The analog computation is suit- able for graph
processing because: 1) The algorithms are iterative and could inherently
tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and
Collaborative Filtering) and typical graph algorithms involving integers (e.g.,
BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a
vertex program of a graph algorithm can be expressed in sparse matrix vector
multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We
show that this assumption is generally true for a large set of graph
algorithms. GRAPHR is a novel accelerator architecture consisting of two
components: memory ReRAM and graph engine (GE). The core graph computations are
performed in sparse matrix format in GEs (ReRAM crossbars). The
vector/matrix-based graph computation is not new, but ReRAM offers the unique
opportunity to realize the massive parallelism with unprecedented energy
efficiency and low hardware cost. With small subgraphs processed by GEs, the
gain of performing parallel operations overshadows the wastes due to sparsity.
The experiment results show that GRAPHR achieves a 16.01x (up to 132.67x)
speedup and a 33.82x energy saving on geometric mean compared to a CPU baseline
system. Com- pared to GPU, GRAPHR achieves 1.69x to 2.19x speedup and consumes
4.77x to 8.91x less energy. GRAPHR gains a speedup of 1.16x to 4.12x, and is
3.67x to 10.96x more energy efficiency compared to PIM-based architecture.Comment: Accepted to HPCA 201
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
With the rise of artificial intelligence in recent years, Deep Neural
Networks (DNNs) have been widely used in many domains. To achieve high
performance and energy efficiency, hardware acceleration (especially inference)
of DNNs is intensively studied both in academia and industry. However, we still
face two challenges: large DNN models and datasets, which incur frequent
off-chip memory accesses; and the training of DNNs, which is not well-explored
in recent accelerator designs. To truly provide high throughput and energy
efficient acceleration for the training of deep and large models, we inevitably
need to use multiple accelerators to explore the coarse-grain parallelism,
compared to the fine-grain parallelism inside a layer considered in most of the
existing architectures. It poses the key research question to seek the best
organization of computation and dataflow among accelerators. In this paper, we
propose a solution HyPar to determine layer-wise parallelism for deep neural
network training with an array of DNN accelerators. HyPar partitions the
feature map tensors (input and output), the kernel tensors, the gradient
tensors, and the error tensors for the DNN accelerators. A partition
constitutes the choice of parallelism for weighted layers. The optimization
target is to search a partition that minimizes the total communication during
training a complete DNN. To solve this problem, we propose a communication
model to explain the source and amount of communications. Then, we use a
hierarchical layer-wise dynamic programming method to search for the partition
for each layer.Comment: To appear in the 2019 25th International Symposium on
High-Performance Computer Architecture (HPCA 2019
Hotspots and difficulties of biliary surgery in older patients
Abstract. With the accelerated aging society in China, the incidence of biliary surgical diseases in the elderly has increased significantly. The clinical characteristics of these patients indicate that improving treatment outcomes and realizing healthy aging are worthy of attention. How to effectively improve the treatment effect of geriatric biliary surgical diseases has attracted widespread attention. This paper reviews and comments on the hotspots and difficulties of biliary surgery in older patients from six aspects: (1) higher morbidity associated with an aging society, (2) prevention and control of pre-operative risks, (3) extending the indications of laparoscopic surgery, (4) urgent standardization of minimally invasive surgery, (5) precise technological progress in hepatobiliary surgery, and (6) guarantee of peri-operative safety. It is of great significance to fully understand the focus of controversy, actively make use of its favorable factors, and effectively avoid its unfavorable factors, for further improving the therapeutic effects of geriatric biliary surgical diseases, and thus benefits the vast older patients with biliary surgical diseases. Accordingly, a historical record with the highest age of 93 years for laparoscopic transcystic common bile duct exploration has been created by us recently