Worn Out EMMC Chips Are Crippling Older Teslas

October 17, 2019 by Tom Nardi 230 Comments

It should probably go without saying that the main reason most people buy an electric vehicle (EV) is because they want to reduce or eliminate their usage of gasoline. Even if you aren’t terribly concerned about your ecological footprint, the fact of the matter is that electricity prices are so low in many places that an electric vehicle is cheaper to operate than one which burns gas at $2.50+ USD a gallon.

Another advantage, at least in theory, is reduced overal maintenance cost. While a modern EV will of course be packed with sensors and complex onboard computer systems, the same could be said for nearly any internal combustion engine (ICE) car that rolled off the lot in the last decade as well. But mechanically, there’s a lot less that can go wrong on an EV. For the owner of an electric car, the days of oil changes, fouled spark plugs, and the looming threat of a blown head gasket are all in the rear-view mirror.

Unfortunately, it seems the rise of high-tech EVs is also ushering in a new era of unexpected failures and maintenance woes. Case in point, some owners of older model Teslas are finding they’re at risk of being stranded on the side of the road by a failure most of us would more likely associate with losing some documents or photos: a disk read error.

Continue reading “Worn Out EMMC Chips Are Crippling Older Teslas” →

Uncertain Future Of Orphaned Jibo Robots Presents Opportunities

March 8, 2019 by Roger Cheng 17 Comments

In our modern connected age, our devices have become far more powerful and useful when they could draw upon resources of a global data network. The downside of a cloud-connected device is the risk of being over-reliant on computers outside of our own control. The people who brought a Jibo into their home got a stark reminder of this fact when some (but not all) Jibo robots gave their owners a farewell message as their servers are shut down, leaving behind little more than a piece of desktop sculpture.

Jibo launched their Indiegogo crowdfunding campaign with the tagline “The World’s First Social Robot For The Home.” Full of promises of how Jibo will be an intelligent addition to a high tech household, it has always struggled to justify its price tag. It cost as much as a high end robot vacuum, but without the house cleaning utility. Many demonstrations of a Jibo’s capabilities centered around its voice control, which an Amazon Echo or Google Home could match at a fraction of the price.

By the end of 2018, all assets and intellectual property have been sold to SQN Venture Partners. They have said little about what they planned to do with their acquisition. Some Jibo owner still hold hope that there’s still a bright future ahead. Both on the official forums (for however long that will stay running) and on unofficial channels like Reddit. Other owners have given up and unplugged their participation in this social home robotics experiment.

If you see one of these orphans in your local thrift store for a few bucks, consider adopting it. You could join the group hoping for something interesting down the line, but you’re probably more interested in its hacking potential: there is a Nvidia Jetson inside good for running neural networks. Probably a Tegra K1 variant, because Jibo used the Jetson TK1 to develop the robot before launch. Jibo has always promised a developer SDK for the rest of us to extend Jibo’s capabilities, but it never really materialized. The inactive Github repo mainly consists of code talking to servers that are now offline, not much dealing directly with the hardware.

Jibo claimed thousands were sold and, if they start becoming widely available inexpensively, we look forward to a community working to give new purpose to these poor abandoned robots. If you know of anyone who has done a teardown to see exactly what’s inside, or if someone has examined upgrade files to create custom Jibo firmware, feel free to put a link in the comments and help keep these robots out of e-waste.

If you want to experiment with power efficient neural network accelerators but rather work with an officially supported development platform, we’ve looked at the Jetson TK1 successors TX1 and TX2. And more recently, Google has launched one of their own, as has our friends at Beaglebone.

Nvidia Brings Computer Vision And Deep Learning To The Embedded World

November 10, 2015 by Brian Benchoff 101 Comments

Today, Nvidia announced their latest platform for advanced technology in autonomous machines. They’re calling it the Jetson TX1, and it puts modern GPU hardware in a small and power efficient module. Why would anyone want GPUs in an embedded format? It’s not about frames per second; instead, Nvidia is focusing on high performance computing tasks – specifically computer vision and classification – in a platform that uses under 10 Watts.

For the last several years, tiny credit card sized ARM computers have flooded the market. While these Raspberry Pis, BeagleBones, and router-based dev boards are great for running Linux, they’re not exactly very powerful. x86 boards also exist, but again, these are lowly Atoms and other Intel embedded processors. These aren’t the boards you want for computationally heavy tasks. There simply aren’t many options out there for high performance computing on low-power hardware.

Tiny ARM computers the size of a credit card have served us all well for general computing tasks, and this leads to the obvious question – what is the purpose of putting so much horsepower on such a small board. The answer, at least according to Nvidia, is drones, autonomous vehicles, and image classification.

Image classification is one of the most computationally intense tasks out there, but for autonomous robots, there’s no other way to tell the difference between a cyclist and a mailbox. To do this on an embedded platform, you either need to bring a powerful general purpose CPU that sucks down 60 or so Watts, or build a smaller, more efficient GPU-based solution that sips a meager 10 Watts.

Continue reading “Nvidia Brings Computer Vision And Deep Learning To The Embedded World” →