8 research outputs found
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
Energy efficient thermal management of data centers via open multi-scale design
Data centers are computing infrastructure facilities that house arrays of electronic racks containing high power dissipation data processing and storage equipment whose temperature must be maintained within allowable limits. In this research, the sustainable and reliable operations of the electronic equipment in data centers are shown to be possible through the Open Engineering Systems paradigm. A design approach is developed to bring adaptability and robustness, two main features of open systems, in multi-scale convective systems such as data centers. The presented approach is centered on the integration of three constructs: a) Proper Orthogonal Decomposition (POD) based multi-scale modeling, b) compromise Decision Support Problem (cDSP), and c) robust design to overcome the challenges in thermal-fluid modeling, having multiple objectives, and inherent variability management, respectively. Two new POD based reduced order thermal modeling methods are presented to simulate multi-parameter dependent temperature field in multi-scale thermal/fluid systems such as data centers. The methods are verified to achieve an adaptable, robust, and energy efficient thermal design of an air-cooled data center cell with an annual increase in the power consumption for the next ten years. Also, a simpler reduced order modeling approach centered on POD technique with modal coefficient interpolation is validated against experimental measurements in an operational data center facility.Ph.D.Committee Co-Chair: Joshi, Yogendra; Committee Co-Chair: Mistree, Farrokh; Committee Member: Hamann, Hendrik F.; Committee Member: Allen, Janet K.; Committee Member: Iyengar, Madhusudan K.; Committee Member: Schwan, Karsten; Committee Member: Stoesser, Thorste