29 research outputs found
Thermal properties of metal matrix composites with planar distribution of carbon fibres
High thermal conductivity (TC) and a tunable coefficient of thermal expansion
are essential properties for heat management materials operating in a wide
temperature range. We combine both properties in a composite with a
low‐density metal matrix reinforced with pitch‐based carbon fibres. The
thermal conductivity of the metal matrix was increased by 50%, the thermal
expansion coefficient was reduced by a factor of five. The samples were
produced by powder metallurgy and have a planar random distribution of fibres,
leading to high performance in two dimensions
Family and Gender Values in China
Previous research has reported on structural changes in Chinese families. However, questions remain as to whether/how social change has influenced family and gender values and how this differs across generations, regions, and gender in China. Drawing on 2006 data from the China General Social Survey, we find that values pertaining to filial piety are traditional, whereas patrilineal and gender values are less traditional. Historic events/policies provide the context for how social change can shape differential generational, geographic, and gender perspectives. Our hypothesis that generation, region, and gender associations will differ across the various ideational domains is confirmed. We find significant interaction effects in how generation and geography differ by gender in patrilineal, filial piety, and gender values; and higher education erodes patrilineal and traditional gender values but enhances filial piety. Such findings indicate that family values should be understood in the specific sociocultural contexts governing Chinese families across time and place.</jats:p
In-Datacenter Performance Analysis of a Tensor Processing Unit
Many architects believe that major improvements in cost-energy-performance
must now come from domain-specific hardware. This paper evaluates a custom
ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since
2015 that accelerates the inference phase of neural networks (NN). The heart of
the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak
throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed
on-chip memory. The TPU's deterministic execution model is a better match to
the 99th-percentile response-time requirement of our NN applications than are
the time-varying optimizations of CPUs and GPUs (caches, out-of-order
execution, multithreading, multiprocessing, prefetching, ...) that help average
throughput more than guaranteed latency. The lack of such features helps
explain why, despite having myriad MACs and a big memory, the TPU is relatively
small and low power. We compare the TPU to a server-class Intel Haswell CPU and
an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters.
Our workload, written in the high-level TensorFlow framework, uses production
NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters'
NN inference demand. Despite low utilization for some applications, the TPU is
on average about 15X - 30X faster than its contemporary GPU or CPU, with
TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the
TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and
200X the CPU.Comment: 17 pages, 11 figures, 8 tables. To appear at the 44th International
Symposium on Computer Architecture (ISCA), Toronto, Canada, June 24-28, 201