Search results with tag "Curette"
arXiv:1512.00567v3 [cs.CV] 11 Dec 2015
arxiv.orgtecture: the first layer is a 3 3 convolution, the second is a fully connected layer on top of the 3 3 output grid of the first layer (see Figure 1). Sliding this small network over the input activation grid boils down to replacing the 5 5 convolution with two layers of …
EfficientNet: Rethinking Model Scaling for Convolutional ...
arxiv.orgtecture search becomes increasingly popular in designing efficient mobile-size ConvNets (Tan et al.,2019;Cai et al., 2019), and achieves even better efficiency than hand-crafted mobile ConvNets by extensively tuning the network width, depth, convolution kernel types and sizes. However, it is unclear how to apply these techniques for larger ...
ATmega32A - Microchip Technology
ww1.microchip.comtecture. The ATmega32 A is a 40/44-pins device with 32 KB Fl ash, 2 KB SRAM and 1 KB EEPROM. By exe-cuting instructions in a single clock cycle, the devices achieve CPU throughput approaching one million instructions per second (MIPS) per megahertz, allowing the system designer to optimize power consump-tion versus processing speed.
New Transport Network Architectures for 5G RAN - Fujitsu
www.fujitsu.comnetwork. While many 4G assets can be reused, it will not be sufficient to overlay 5G on a 4G backhaul architecture, and even where an operator has deployed an advanced C-RAN archi-tecture, new investment is required to meet 5G requirements. 5G RAN introduces new physical topologies, more functional split options, and ultra-low-
Intel SGX Explained
eprint.iacr.orgtecture, where the OS kernel and hypervisor manage the computer’s resources. This work discusses the original version of SGX, also referred to as SGX 1. While SGX 2 brings very useful Trusted Platform Secure Container Data OwnerÕs Computer Initial State Public Code + Data Key exchange: B, g A Shared key: K = g AB Key exchange: A, g A g A g B ...
Transformer-XL: Attentive Language Models beyond a Fixed ...
aclanthology.orgtecture is able to substantially improve the evalua-tion speed. 3.2 Segment-Level Recurrence with State Reuse To address the limitations of using a fixed-length context, we propose to introduce a recurrence mechanism to the Transformer architecture. Dur-ing training, the hidden state sequence computed for the previous segment is fixed and ...
Innovus Implementation System
www.cadence.comtecture, which supports multi-threaded tasks simultaneously on multiple CPUs, is designed such that the system can produce best-in-class TAT with standard hardware, which is normally 8-16 CPUs per box. In addition, for designs with a larger instance count, the flow can scale over a larger number of CPUs. The system’s
Abstract arXiv:1611.05431v2 [cs.CV] 11 Apr 2017
arxiv.orgWe present a simple, highly modularized network archi-tecture for image classification. Our network is constructed by repeating a building block that aggregates a set of trans-formations with the same topology. Our simple design re-sults in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy ...
A arXiv:1609.02907v4 [cs.LG] 22 Feb 2017
arxiv.orgWe motivate the choice of our convolutional archi-tecture via a localized first-order approximation of spectral graph convolutions. ... A neural network model based on graph convolutions can therefore be built by stacking multiple convolutional layers of the form of Eq. 5, each layer followed by a point-wise non-linearity. ...