AI Technology & Industry Review
A research team from NVIDIA, Stanford University and Microsoft Research propose a novel pipeline parallelism approach that improves throughput by more than 10 percent with a comparable memory footprint, showing such strategies can achieve high aggregate throughput while training models with up to a trillion parameters.
Nvidia CEO Jensen Huang today unveiled the company’s new GeForce RTX 30 Series GPUs.
The A100 represents the largest leap in performance across the company’s eight GPU generations - a boost of up to 20x over its predecessors.
The new goal of the Folding@home project is modelling the structure of SARS-CoV-2 spike protein to identify sites that can be targeted by a therapeutic antibody.
A recent paper accepted by ICLR 2020 proposes a new transformer model called “Reformer” which achieves impressive performance even when running on only a single GPU.
The US chip giant now appears to be exploring the terrain beyond GPU architecture, raising the stakes on other dedicated electronic circuits.
A research team from NVIDIA, Oak Ridge National Laboratory (ORNL), and Uber has introduced new techniques that enabled them to train a fully convolutional neural network on the world’s fastest supercomputer, Summit, with up to 27,600 NVIDIA GPUs.
Because training deep learning models requires intensive computation, AI researchers are always on the lookout for new and better hardware and software platforms for their increasingly sophisticated models.
Thanks to the CUDA architecture [1] developed by NVIDIA, developers can exploit GPUs’ parallel computing power to perform general computation without extra efforts. Our objective is to evaluate the performance achieved by TensorFlow, PyTorch, and MXNet on Titan RTX.
No wow moments, no bells, and no whistles. Jensen Huang has delivered some groundbreaking keynote speeches in his years at the helm of NVIDIA, but today’s was not among them.
Information retrieval (IR) is the activity of retrieving information from a collection of sources stored on computers, based on user queries. IR enjoys a history of one century [1], and serves as the heart of many ubiquitous applications such as web search, product recommendation, and personal feeds on social networks.
Apple has unveiled the latest iteration of its smartphone chip: the A12 Bionic SoC (system-on-a-chip). The company made the announcement yesterday at its annual product showcase event in Cupertino, California, hailing the A12 as the industry’s first ever 7nm chip (the smallest current transistor scale). It will be embedded in Apple’s new XR, XS, and XS Max iPhones.
At the IFA 2018 in Berlin today Huawei unveiled the new Kirin 980, the world’s first 7nm smartphone SoC (system-on-a-chip) with a dual-core NPU (Neural Network Processing Unit).
Nvidia Founder and CEO Jensen Huang today unveiled the company’s long-waited next-generation graphic processing unit (GPU) cards for games, Geforce RTX.
At the prestigious SIGGRAPH (Special Interest Group on Computer GRAPHics and Interactive Techniques) conference in Vancouver yesterday, Nvidia CEO Jensen Huang announced Turing, an eighth-generation GPU architecture introducing ray tracing and AI capability to real-time graphics.
DeepMind’s 2018 AlphaGo Zero requires 300,000 times more computing power than AlexNet did in 2013. With larger-than-ever datasets and demandingContinue Reading
In the drive to unleash the full potential of artificial intelligence, data transmission has thus far been mostly overlooked. It was not viewed as a serious issue until the recent rise of AI ratcheted up demand for data processing.
“The world of computing has changed,” announced Nvidia founder and CEO Jensen Huang this week as he unveiled the new NVIDIA HGX-2 Cloud Server Platform at his company’s GPU Technology Conference in Taiwan.
Huang and his NVIDIA team pioneered the graphics processing chip (GPU) in 1999, revolutionizing the visual performance of device displays. But even Huang never dreamt that his GPU would one day become a driving force in the arena of AI.
Chip giant NVIDIA Founder and CEO Jensen Huang created a bit of a stir at yesterday’s GPU Technology Conference in Santa Clara, USA, when he appeared to dis one of these chips’ appropriateness for autonomous vehicle system development: “FPGA is not the right answer,” he said.
At the GPU Technology Conference in Santa Clara, USA today, Huang unveiled the world’s largest GPU — a binary beast packed with 16 Tesla V100 with doubled memory 32 GB.
Embedded AI can transform a tabletop speaker into a personal assistant; give a robot brains and dexterity; and turn a smartphone into a smart camera, music player, or game console. Traditional processors, however, lack the computational power to support many of these intelligent features.
To boost CNN performance, industry leaders and startups alike are now racing to develop specialized AI chips.
On November 6 in Beijing, China’s rising semiconductor unicorn Cambricon released new state-of-art AI processors.
The annual NVIDIA GTA Conference opened in Beijing on September 26th.
China’s biggest domestic mobile phone maker, Huawei, is now powering its phones with artificial intelligence (AI) engines. The Chinese techContinue Reading
In this report, we will touch on some of the recent technologies, trends and studies on deep neural network inference acceleration and continuous training in the context of production systems.
At this year’s GPU Technology Conference, Nvidia CEO and Founder Jensen Huang unveiled a new generation GPU architecture called Volta.
“We want to be able to train a machine learning model, like Google Translate, in a few hours,” said Prof. Hwu. Best known for enabling the parallelism capability of the GPU to solve scientific problems like AI.
The article discusses about the limitation of using existing GPU to train neural network and how one can improve to train larger model.
In this article we will provide some insights of Intel’s recent deep learning products.
This talk is focused on the future potential of deep learning with NVIDIA Deep Learning SDK and GPU hardware families.
In a tech talk at University of Toronto, NVIDIA shared some updates regarding their research of self-driving car and End-to-End Learning