Le automobili auto-guidate del futuro saranno mosse da NVidia.

Presentato lo scorso Gennaio durante il Consumer Electronic Show, Nvidia Drive PX è lo strumento che mette a disposizione delle case automobilistiche le possibilità offerte dal Deep Learning nella creazione di automobili auto-guidate, ed è proprio alla GPU Technology Conference 2015 che Nvidia annuncia che ne renderà ufficiale la disponibilità a partire da maggio per tutte le cause automobilistiche di prima categoria e per le istituzioni di ricerca.

Potete trovare maggiori informazioni in merito nel post pubblicato sul blog ufficiale NVidia che potete trovare qui di seguito. E no, purtroppo non avremo a breve la nostra KITT privata… 

How NVIDIA DRIVE PX Will Help Automakers Slim Down Self-Driving Cars

The future, as the saying goes, is already here. It’s just unevenly distributed. Maybe that’s because it comes loaded with a trunk full of computers.

So today we’re announcing pricing and availability for our NVIDIA PX development platform.

Introduced at the Consumer Electronics Show in January, NVIDIA DRIVE PX is a powerful self-driving car computer designed to slip the power of deep neural networks into real-world cars.

We’re making our DRIVE PX development platform available in May to automakers, Tier 1 automotive suppliers and research institutions, to get cars on the road toward driving themselves.

NVIDIA DRIVE PX is built for the growing number of automakers that have already put – or soon will put – self-driving cars on the roads.

The common denominator: all of these projects rely on NVIDIA GPU technology to help process and analyze, in real time, the data streaming in from sensors and cameras mounted all over the car.

One of our partners has even announced plans to send off its self-piloted cars across the United States.

DRIVE PX’s twin NVIDIA Tegra X1 processors deliver 2.3 teraflops of performance. Yet each individual superchip is no bigger than a thumbnail.
textNVIDIA DRIVE PX’s twin Tegra X1 processors deliver 2.3 teraflops of power.

That’s enough to weave together data streaming in from 12 camera inputs and enable a wide range of advanced driver assistance features to run simultaneously – including surround view, collision avoidance, pedestrian detection, mirror-less operation, cross-traffic monitoring and driver-state monitoring.

More Is More

Yet DRIVE PX is built to tap into a new technology called “deep learning” to give it capabilities far beyond what you can stuff into any of today’s passenger cars.

That’s because today’s advanced driver assistance systems have evolved around the principle of classifying objects that the car’s sensors would detect.

It works, but it’s not enough. Imagine training such a system to be ready for any possible eventuality. It’s just not possible.

Our DRIVE PX development platform is built to crack that problem. It includes a new deep neural network software development kit we call DIGITS, as well as video capture and video processing libraries.

DIGITS is a deep learning training system that can be run on systems powered by our GPUs – including our new DIGITS DevBox development platform – and that lets computers train themselves to understand objects in the world around them ((see “DIGITs: Deep Learning Training System” on our Parallel Forall blog for more details).

Much like a human learns through experiences, so does the deep neural network. Now we can do more than just train systems to recognize objects – but also training behavior. (See “Here’s How Deep Learning Will Accelerate Self-Driving Cars” for an overview of how this works.)

Then the model created by the DIGITS DevBox can be loaded into a vehicle and run in real time on the DRIVE PX.

It’s a system that can be trained, and retrained, with more data. Every time your self-driving car gets an over-the-air update, it can get smarter.

The result: a self-driving system that extends well beyond the hardware found in any car, without all that extra electronic junk in the trunk.

Un futuro certo per lo sviluppo di nuove tecnologie.

L’ architettura Pascal di Nvidia debuterrà l’anno prossimo e, stando a quanto affermato durante la GPU Technology Conference di quest’anno dal CEO di Nvidia, Jen-Hsun Huang, riuscirà a rendere le applicazioni dedicate al Deep Learning (Apprendimento Approfondito) fino a 10 volte più veloci rispetto alle performance attualmente offerte dai processori Maxwell; merito, stando a quanto affermato, della maggiore memoria disponibile – fino a 32Gb (2.7 volte in più di Titan X) – e della memoria tri-dimensionale.

Per maggiori informazioni potete consultare il post pubblicato sul blog ufficiale NVidia qui di seguito:

NVIDIA’s Next-Gen Pascal GPU Architecture to Provide 10X Speedup for Deep Learning Apps

NVIDIA‘s Pascal GPU architecture, set to debut next year, will accelerate deep learning applications 10X beyond the speed of its current-generation Maxwell processors.

NVIDIA CEO and co-founder Jen-Hsun Huang revealed details of Pascal and the company’s updated processor roadmap in front of a crowd of 4,000 during his keynote address at the GPU Technology Conference, in Silicon Valley.

“It will benefit from a billion dollars worth of refinement because of R&D done over the last three years,” he told the audience.

The rise of deep learning – the process by which computers use neural networks to teach themselves – led NVIDIA to evolve the design of Pascal, which was originally announced at last year’s GTC.

Pascal GPUs will have three key design features that will result in dramatically faster, more accurate training of richer deep neural networks – the human cortex-like data structures that serve as the foundation of deep learning research.

Along with up to 32GB of memory — 2.7X more than the newly launched NVIDIA flagship, the GeForce GTX TITAN X — Pascal will feature mixed-precision computing. It will have 3D memory, resulting in up to 5X improvement in deep learning applications. And it will feature NVLink – NVIDIA’s high-speed interconnect, which links together two or more GPUs — that will lead to a total 10X improvement in deep learning.

TextPascal will offer better performance than Maxwell on key deep-learning tasks.

Mixed-Precision Computing – for Greater Accuracy

Mixed-precision computing enables Pascal architecture-based GPUs to compute at 16-bit floating point accuracy at twice the rate of 32-bit floating point accuracy.

Increased floating point performance particularly benefits classification and convolution – two key activities in deep learning – while achieving needed accuracy.

3D Memory – for Faster Communication Speed and Power Efficiency

Memory bandwidth constraints limit the speed at which data can be delivered to the GPU. The introduction of 3D memory will provide 3X the bandwidth and nearly 3X the frame buffer capacity of Maxwell. This will let developers build even larger neural networks and accelerate the bandwidth-intensive portions of deep learning training.

Pascal will have its memory chips stacked on top of each other, and placed adjacent to the GPU, rather than further down the processor boards. This reduces from inches to millimeters the distance that bits need to travel as they traverse from memory to GPU and back. The result is dramatically accelerated communication and improved power efficiency.

NVLink – for Faster Data Movement

The addition of NVLink to Pascal will let data move between GPUs and CPUs five to 12 times faster than they can with today’s current standard, PCI-Express. This is greatly benefits applications, such as deep learning, that have high inter-GPU communication needs.

NVLink allows for double the number of GPUs in a system to work together in deep learning computations. In addition, CPUs and GPUs can connect in new ways to enable more flexibility and energy efficiency in server design compared to PCI-E.

Nuove risorse per dare potenza al Deep Learning.

Durante il keynote di apertura della GPU Technology Conference del 2015, il CEO di Nvidia Jen-Hsun Huang ha dato dimostrazione di tre delle funzionalità che riusciranno a donare nuova vita al Deep Learning (Apprendimento Approfondito) – branca dell’Intelligenza Artificiale principalmente dedita alle tematiche della medicina avanzata e della farmaceutica oltre che alla base delle automobili auto-guidate -, si tratta di:

– Nvidia GeForce GTX Titan X, la scheda video che monta il più potente processore dedicato all’addestramento di reti neurali approfondite;

– DIGITS Deep Learning GPU Training System, un applicazione in grado di permettere agli scienzati ed ai ricercatori di creare reti neurali approfondite;

– DIGITS DevBox, già pronto con il DIGITS Deep Learning GPU Training System, si tratta della più potente macchina dedicata al Deep Learning che sfrutta la potenza di 4 NVidia GeForce GTX Titan X;

Per maggiori informazioni potete consultare il post del blog qui di seguito oppure visitare direttamente la pagina del blog NVidia:

NVIDIA Propels Deep Learning with TITAN X, New DIGITS Training System and DevBox

Addressing one of today’s most sophisticated technology challenges, we just unveiled new hardware and software that bring unprecedented speed, ease and power to deep learning research.

Deep learning – a rapidly growing segment of artificial intelligence – is an engine of computing innovation for areas as diverse as advanced medical and pharmaceutical research to fully autonomous, self-driving cars.

NVIDIA CEO and co-founder Jen-Hsun Huang showcased three new technologies that will fuel deep learning during his opening keynote address to the 4,000 attendees of the GPU Technology Conference:

NVIDIA GeForce GTX TITAN X – the most powerful processor ever built for training deep neural networks.
DIGITS Deep Learning GPU Training System – a software application that makes it far easier for data scientists and researchers to quickly create high-quality deep neural networks.
DIGITS DevBox – the world’s fastest deskside deep learning appliance — purpose-built for the task, powered by four TITAN X GPUs and loaded with the intuitive-to-use DIGITS training system.

Another Side of TITAN X
textDouble life: Titan X can spin elaborate virtual worlds or blast through heavy-duty science.

TITAN X is our new flagship GeForce gaming GPU, but it’s also uniquely suited for deep learning.

We gave a sneak peek of TITAN X two weeks ago at the Game Developers Conference, in San Francisco, where it drove a stunning virtual reality experience called “Thief in the Shadows,” based on the dragon Smaug, from “The Hobbit.”

The latest AAA titles are breathtaking on TITAN X in 4K. Middle-earth: Shadow of Mordor, for example, runs at 40 frames per second on high settings with FXAA enabled, compared with 30fps on the GeForce GTX 980, released in September.

Built on the NVIDIA Maxwell GPU architecture, TITAN X delivers twice the performance and double the power efficiency of its predecessor by combining 3,072 processing cores for 7 teraflops of peak single-precision performance with 12GB of onboard memory.

With that processing power and 336.5GB/s of memory bandwidth, it can rip through the millions of pieces of data used to train deep neural networks. On AlexNet, an industry-standard model, for example, TITAN X took less than three days to train the model using the 1.2 million image ImageNet dataset, compared with over 40 days for a 16-core CPU.

Available today, TITAN X is selling for just $999.

DIGITS: Quick, Easy Path to the Best Deep Neural Net

Using deep neural networks to train computers to teach themselves how to classify and recognize objects can be an onerous, time-consuming task.

The DIGITS Deep Learning GPU Training System software changes that by giving users what they need from start to finish to build the best possible deep neural nets.

Available for download at http://developer.nvidia.com/digits, it’s the first all-in-one graphical system for designing, training and validating deep neural networks for image classification.

Titan X for Deep Learning
DIGITS guides users through the process of setting up, configuring and training deep neural networks – handling the heavy lifting so that scientists can focus on the research and results.

Preparing and loading training data sets with DIGITS – whether on a local system or from the web – is simple, thanks to its intuitive user interface and workflow management capabilities.

It’s the first system of its kind to provide real-time monitoring and visualization, so users can fine tune their work. And it supports the GPU-accelerated version of Caffe, the popular framework used by many data scientists and researchers today to build neural nets (see “DIGITs: Deep Learning Training System” on our Parallel Forall blog for more details).
textDIGITS guides users through the process of setting up, configuring and training deep neural networks – handling the heavy lifting so that scientists can focus on the research and results.

DIGITS DevBox: World’s Fastest Deskside Deep Learning Machine

Built by the NVIDIA deep learning engineering team for its own R&D work, the DIGITS DevBox is an all-in-one powerhouse of a platform for speeding up deep learning research.

Starting with its four TITAN X GPUs, every component of the DevBox – from memory to I/O to power – has been optimized to deliver highly efficient performance for the toughest deep learning research.
textBuilt to Go Deep: Every component of our DIGIT DevBox has been optimized for deep learning research.

It comes preinstalled with all the software data scientists and researchers require to develop their own deep neural networks. This includes the DIGITS software package, the most popular deep learning frameworks – Caffe, Theano and Torch – and cuDNN 2.0, NVIDIA’s robust GPU-accelerated deep learning library.

And it’s all wrapped up in an energy-efficient, quiet, cool-running — and great-looking — package that fits under a desk and plugs into an ordinary wall socket. We’re selling this research powerhouse for $15,000.

Very early results of multi-GPU training show the DIGITS DevBox delivers almost 4X higher performance than a single TITAN X on key d
eep learning benchmarks. Training AlexNet can be completed in only 13 hours with the DIGITS DevBox, compared to over 2 days with the best single GPU PC, or over a month with a CPU-only system.