Deep neural networks (DNNs) have been widely applied in diverse applications, but the problems of high latency and energy overhead are inevitable on resource-constrained devices. To address this challenge, most researchers focus on the dynamic voltage and frequency scaling (DVFS) technique to balance the latency and energy consumption by changing the computing frequency of processors. However, the adjustment of memory frequency is usually ignored and not fully utilized to achieve efficient DNN inference, which also plays a significant role in the inference time and energy consumption. In this paper, we first investigate the impact of joint memory frequency and computing frequency scaling on the inference time and energy consumption with a model-based and data-driven method. Then by combining with the fitting parameters of different DNN models, we give a preliminary analysis for the proposed model to see the effects of adjusting memory frequency and computing frequency simultaneously. Finally, simulation results in local inference and cooperative inference cases further validate the effectiveness of jointly scaling the memory frequency and computing frequency to reduce the energy consumption of devices.

Turns out he's even better seated behind his workbench, as the completely custom auto-tracking gimbal he came up with is nothing short of a work of art. There's quite a bit going on here, and as you might expect, it took several iterations before [Gabriel] got all the parts working together. The rather GLaDOS-looking body of the gimbal is entirely 3D printed, and holds the motors, camera, and a collection of ultrasonic receivers. The Nvidia Jetson TX1 that does the computational heavy lifting is riding shotgun in its own swanky looking 3D printed enclosure, but [Gabriel] notes a future revision of the hardware should be able to reunite them. In the current version of the system, the target wears an ultrasonic emitter that is picked up by the sensors in the gimbal.

A demo of achieving real-time deep object detection using DetectNet on the Jetson TX1 via DarwinAI technology. The top half shows DetectNet running on the Jetson TX1, while the bottom half shows an optimized, evolved DetectNet created using DarwinAI technology running on the Jetson TX1. Find out more about DarwinAI technology at: http://www.darwinai.ml

Expanding on their Jetson TX1 and TK1 products for embedded computing, NVIDIA announced last week their Jetson TX2 platform--a hardware and software platform the size of a credit card designed to deliver AI computing at the edge. NVIDIA touts Jetson TX2 as delivering "unprecedented deep learning capabilities," and based on the form factor, it may be right as it paves the way for a number of cutting-edge uses--from highly intelligent factory robots and commercial drones, to cameras with AI for smart cities. NVIDIA has been running on all cylinders lately with datacenter machine learning, and I think this release, if it performs as promised, will solidify their place at the top of the machine learning class in certain classes of devices. NVIDIA announced the TX2 at an event I attended last week in San Francisco with many tier 1 vendors and startups with some interesting use cases. Jetson, by design, isn't targeted at every embedded device, it's for those non-mobile devices who need strong deep neural network performance at a given power draw. The TX2 is a significant step up from its predecessor.

Expanding on their Jetson TX1 and TK1 products for embedded computing, NVIDIA announced last week their Jetson TX2 platform--a hardware and software platform the size of a credit card designed to deliver AI computing at the edge. NVIDIA touts Jetson TX2 as delivering "unprecedented deep learning capabilities," and based on the form factor, they may be right as it paves the way for a number of cutting-edge uses--from highly intelligent factory robots and commercial drones, to cameras with AI for smart cities. NVIDIA has been running on all cylinders lately with datacenter machine learning, and I think this release, if it performs as promised, will solidify their place at the top of the machine learning class in certain classes of devices. NVIDIA announced the TX2 at an event I attended last week in San Francisco with many tier 1 vendors and startups with some interesting use cases. Jetson, by design, isn't targeted at every embedded device, it's for those non-mobile devices who need strong deep neural network performance at a given power draw. The TX2 is a significant step up from its predecessor.

Get to know some intelligent machines and the developers who built them. Join us for a night of cocktails/appetizers, tech talks, and learn how our partners, developers and start-ups are using the Jetson TX1 AI supercomputer to create intelligent devices to solve tomorrow's problems today. Meet Jetson partners and hear first-hand how they took their projects from idea to reality. Get to know folks from Horus, Parrot and many more --companies that are using Jetson every day! And, of course, we'll have swag.

The Internet of Things (IoT) has been touted as the next Industrial Revolution, with pervasive connectivity and the insights it can generate offering a new digital lens for viewing and managing the physical world. But in addition to the tangible process efficiencies and quality of life improvements expected from the IoT, it's also a stepping stone to perhaps the greatest achievement in human history: artificial intelligence (AI). In many ways the technological progression of AI and the IoT are intertwined. IoT will provide the information that fuels our data-driven economy, while AI is the engine that will consume it. Though both paradigms are still in their infancy, each's success is contingent upon the other's: The IoT can never reach its potential without a mechanism for autonomously processing large heterogeneous data sets, just as AI is incapable of expanding without being fed massive amounts of data.

Fun projects arise when you combine a problem that needs to be solved with a desire to learn. My problem was the neighbors' cats: I wanted to encourage them to hang out somewhere other than my front yard. At the same time, I wanted to learn more about neural network software and deep learning and have a bit of fun doing it. So I set out to build a system that would automatically chase cats out of my yard using artificial intelligence. This boils down to a neural network trained to recognize cats in images from a security camera and turn on the sprinkler system to scare them away. The neural network inference runs on Jetson TX1. This post is an elaboration on and continuation of my notes on the project here.

The 2016 Embedded Vision Summit recently took place in the heart of Silicon Valley. The summit started with a bang when Jeff Dean announced some impressive results using reduced precision deep learning models for inference. For embedded and edge applications of deep learning models, reduced precision inference is a big deal. A brief primer is that model size is reduced by four times since normally single precision uses 32 bits per value. The power draw is significantly reduced as 16 bit arithmetic is nearly two times as fast and memory transfers can account for the majority of the power budget.

TAIPEI, TAIWAN--(Marketwired - May 31, 2016) - Computex - NVIDIA (NASDAQ: NVDA) won big at the Computex Best Choice Awards, with the NVIDIA Tesla M40 GPU and NVIDIA Jetson TX1 module hauling in Gold Awards and the NVIDIA SHIELD Android TV clinching a Category Award. Garnering these three prestigious awards extends the company's winning streak -- the longest of any international Computex exhibitor -- to eight consecutive years. Taiwan's President Tsai Ing-wen will hand out the awards. The Best Choice Awards, established in 2002, honor innovation, functionality and market potential. The Gold Award-winning NVIDIA Tesla M40 GPU is the world's fastest deep learning training accelerator.