46 research outputs found
The lost world of Twemlow and Kent-Watson: Mancunian exploitation film in the 1980s and 1990s
The Grizzly, February 13, 2003
Iraqi Speaker Describes his Country as a Prison of Suffering • Ursinus Student Heroes 30 Years Ago • To Fight or not to Fight: You May not Have the Option • Opinions: Skiing Fun Close to Home; Lonely this Valentine\u27s Day? • Female Pride, Guerrilla Warfare • First of Pew Fellows Speaks • Francis Moore Lappe to Visit Ursinus on February 19th • Ursinus Swimmers Look Towards Champions Meet • Men\u27s Basketball Dominate Centennial Conference • Track Handling Business Indoors • Women\u27s Basketball Can\u27t Find the Right Touchhttps://digitalcommons.ursinus.edu/grizzlynews/1529/thumbnail.jp
TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings
In response to innovations in machine learning (ML) models, production
workloads changed radically and rapidly. TPU v4 is the fifth Google domain
specific architecture (DSA) and its third supercomputer for such ML models.
Optical circuit switches (OCSes) dynamically reconfigure its interconnect
topology to improve scale, availability, utilization, modularity, deployment,
security, power, and performance; users can pick a twisted 3D torus topology if
desired. Much cheaper, lower power, and faster than Infiniband, OCSes and
underlying optical components are <5% of system cost and <3% of system power.
Each TPU v4 includes SparseCores, dataflow processors that accelerate models
that rely on embeddings by 5x-7x yet use only 5% of die area and power.
Deployed since 2020, TPU v4 outperforms TPU v3 by 2.1x and improves
performance/Watt by 2.7x. The TPU v4 supercomputer is 4x larger at 4096 chips
and thus ~10x faster overall, which along with OCS flexibility helps large
language models. For similar sized systems, it is ~4.3x-4.5x faster than the
Graphcore IPU Bow and is 1.2x-1.7x faster and uses 1.3x-1.9x less power than
the Nvidia A100. TPU v4s inside the energy-optimized warehouse scale computers
of Google Cloud use ~3x less energy and produce ~20x less CO2e than
contemporary DSAs in a typical on-premise data center.Comment: 15 pages; 16 figures; to be published at ISCA 2023 (the International
Symposium on Computer Architecture
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 Injection for Fast Ignition
In the fast ignition concept, assembled fuel is ignited through a separate high intensity laser pulse. Fast Ignition targets facilitate this ignition using a reentrant cone. It provides clear access through the overlaying coronal plasma, and controls the laser plasma interaction to optimize hot-electron production and transport into the compressed plasma. Recent results suggest that the cone does not play any role in guiding light or electrons to its tip, and coupling to electrons can be reduced by a small amount of preplasma. This puts stringent requirements on the ignition laser focusing, pointing, and prepulse
New filovirus disease classification and nomenclature.
The recent large outbreak of Ebola virus disease (EVD) in Western Africa resulted in greatly increased accumulation of human genotypic, phenotypic and clinical data, and improved our understanding of the spectrum of clinical manifestations. As a result, the WHO disease classification of EVD underwent major revision
25th annual computational neuroscience meeting: CNS-2016
The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong