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I'm definitely not expert and probably this is a dumb question , but why smart edge things like smart robot and not dumb edge with smart central brain ? Anyway

by fvv 6y ago

I'm definitely not expert and probably this is a dumb question , but why smart edge things like smart robot and not dumb edge with smart central brain ? Anyway data are useful aggregated central;ly why not incorporate the brain centrally too?

fvv 6y ago
I mean it could be way more powerful like stadia on phone vs using phone gpu , latency is not too high for the described usage .. imo just automotive may require a dedicated brain on the edge, I'm totally wrong ?
- mebr 6y ago
  I work at a startup that uses edge AI. There are many factors that edge is preferred over cloud. Security is one. Latency is important in many cases. If the internet connection is another dependency for a critical system, it can be a big headache. Once you start working on a real-world project you run into these issues. In return you give up monitoring the data and model that can be done with cloud deployment.
halotrope 6y ago
Cloud GPU/TPU resources are still somewhat expensive. Also bandwidth can be an issue when you would first need to feed video through potentially metered connections. Last but not least latency can be an issue for e.g robotics and automotive.
fluffything 6y ago
Not a dumb question at all: data traffic is expensive. If you have thousands of remote sensors collecting Gbs and GBs of real time data, for ~1000$ you can add a "streaming" supercomputer to your sensor to analyze the data in place and save on network and storage costs. Notice however that the announcement is for an Nvidia AGX product, which is for autonomous machines. The Nvidia "edge" products for processing data on the sensors are called Nvidia EGX. For autonomous machines, you often need to analyze the data in the machine anyways, e.g., you don't want a drone falling if it looses network connectivity.
throwlaplace 6y ago
latency
dejv 6y ago
For things like industrial robots or UAVs latency is biggest problem. I've worked on fruit sorting machine and there was about 20ms to make decision if the object passed or not + there was continuous streams of 10000s of objects per second to classify. The computer vision/classifier had to be both fast and reliable about spitting the answers, which was actually more important than precission of classifier itself.
TaylorAlexander 6y ago
Well, first some clarification - "edge" means "on robot" versus something in the cloud. And the reason you do this is latency and connectivity. I am designing a four wheel drive robot using the NVIDIA AGX Xavier [1] that will follow trails on its own or follow the operator on trails. You don't want your robot to lose cellular coverage and become useless. Even if you had coverage, there would be significant data usage as Rover uses four 4k cameras, which is about 30 megapixels (actually they max out at 13mp each or 52mp total). Constantly streaming that to the cloud would be very expensive on a metered internet connection. Even on a direct line the machine would saturate many broadband connections. Of course you can selectively stream but this makes things more complicated. Latency is an issue. Imagine a self driving car that required a cloud connection. It's approaching an intersection and someone on a bicycle falls over near its path. Better send that sensor data to the cloud fast to determine how to act! On my Rover robot it streams the cameras directly in to the GPU memory where it can be processed using ML without ever being copied through the CPU. It's super low latency and allows for robots that respond rapidly to their environment. Imagine trying to make a ping-pong playing robot with a cloud connection. I am also designing a farming robot. [2] We don't expect any internet connection on farms! [1] https://reboot.love/t/new-cameras-on-rover/ https://reboot.love/t/new-cameras-on-rover/ [2] https://www.twistedfields.com/technology https://www.twistedfields.com/technology Edit: Don’t forget security! Streaming high resolution sensors over the cloud is a security nightmare.
- fizixer 6y ago
  Edge means on-premise (on robot) as you said. But 'edge,' as used in context of AI, is also a wink-and-a-nod that the device is inference-only (no learning, no training). The term "inference only" doesn't sound very marketing-friendly.
  - my123 6y ago
    AGX Xavier can do training on device just fine - and run every CUDA workload. It's just not the fastest device at that, you'd prefer a desktop GPU if you can for such a purpose.
corysama 6y ago
The network is very, very unreliable at the edge. Better to have each piece work independently and store up processed results to transmit eventually, opportunistically. If that processing involves real time video processing there's no way you're going to get that done over a reliably unreliable connection.
bitwize 6y ago
Why do humans carry large, energy-hungry brains around as opposed to being simple tools of the Hivemind like their brethren the insects? Making the edges smarter allows them to react and adapt on smaller timescales.
- goldcd 6y ago
  But we've also developed distributed abilities with quite boggling amounts of specialization. If you chucked one random human on a desert island, they'd probably die. Chuck a dozen, they have a better chance of survival. Chuck a thousand, you might have a civilization. Conversely if you say chucked 2 or 100 rabbits on an island - end result is probably going to be an island full of rabbits.
  - pvg 6y ago
    One Italian plumber and a bunch of rabbids, on the other hand...

by fvv 6y ago

fvv 6y ago

I mean it could be way more powerful like stadia on phone vs using phone gpu , latency is not too high for the described usage .. imo just automotive may require a dedicated brain on the edge, I'm totally wrong ?

mebr 6y ago
I work at a startup that uses edge AI. There are many factors that edge is preferred over cloud. Security is one. Latency is important in many cases. If the internet connection is another dependency for a critical system, it can be a big headache. Once you start working on a real-world project you run into these issues. In return you give up monitoring the data and model that can be done with cloud deployment.

halotrope 6y ago

Cloud GPU/TPU resources are still somewhat expensive. Also bandwidth can be an issue when you would first need to feed video through potentially metered connections. Last but not least latency can be an issue for e.g robotics and automotive.

fluffything 6y ago

Not a dumb question at all: data traffic is expensive. If you have thousands of remote sensors collecting Gbs and GBs of real time data, for ~1000$ you can add a "streaming" supercomputer to your sensor to analyze the data in place and save on network and storage costs. Notice however that the announcement is for an Nvidia AGX product, which is for autonomous machines. The Nvidia "edge" products for processing data on the sensors are called Nvidia EGX. For autonomous machines, you often need to analyze the data in the machine anyways, e.g., you don't want a drone falling if it looses network connectivity.

throwlaplace 6y ago

latency

dejv 6y ago

For things like industrial robots or UAVs latency is biggest problem. I've worked on fruit sorting machine and there was about 20ms to make decision if the object passed or not + there was continuous streams of 10000s of objects per second to classify. The computer vision/classifier had to be both fast and reliable about spitting the answers, which was actually more important than precission of classifier itself.

TaylorAlexander 6y ago

Well, first some clarification - "edge" means "on robot" versus something in the cloud. And the reason you do this is latency and connectivity. I am designing a four wheel drive robot using the NVIDIA AGX Xavier [1] that will follow trails on its own or follow the operator on trails. You don't want your robot to lose cellular coverage and become useless. Even if you had coverage, there would be significant data usage as Rover uses four 4k cameras, which is about 30 megapixels (actually they max out at 13mp each or 52mp total). Constantly streaming that to the cloud would be very expensive on a metered internet connection. Even on a direct line the machine would saturate many broadband connections. Of course you can selectively stream but this makes things more complicated. Latency is an issue. Imagine a self driving car that required a cloud connection. It's approaching an intersection and someone on a bicycle falls over near its path. Better send that sensor data to the cloud fast to determine how to act! On my Rover robot it streams the cameras directly in to the GPU memory where it can be processed using ML without ever being copied through the CPU. It's super low latency and allows for robots that respond rapidly to their environment. Imagine trying to make a ping-pong playing robot with a cloud connection. I am also designing a farming robot. [2] We don't expect any internet connection on farms! [1] https://reboot.love/t/new-cameras-on-rover/ https://reboot.love/t/new-cameras-on-rover/ [2] https://www.twistedfields.com/technology https://www.twistedfields.com/technology Edit: Don’t forget security! Streaming high resolution sensors over the cloud is a security nightmare.

fizixer 6y ago
Edge means on-premise (on robot) as you said. But 'edge,' as used in context of AI, is also a wink-and-a-nod that the device is inference-only (no learning, no training). The term "inference only" doesn't sound very marketing-friendly.
- my123 6y ago
  AGX Xavier can do training on device just fine - and run every CUDA workload. It's just not the fastest device at that, you'd prefer a desktop GPU if you can for such a purpose.

corysama 6y ago

The network is very, very unreliable at the edge. Better to have each piece work independently and store up processed results to transmit eventually, opportunistically. If that processing involves real time video processing there's no way you're going to get that done over a reliably unreliable connection.

bitwize 6y ago

Why do humans carry large, energy-hungry brains around as opposed to being simple tools of the Hivemind like their brethren the insects? Making the edges smarter allows them to react and adapt on smaller timescales.

goldcd 6y ago
But we've also developed distributed abilities with quite boggling amounts of specialization. If you chucked one random human on a desert island, they'd probably die. Chuck a dozen, they have a better chance of survival. Chuck a thousand, you might have a civilization. Conversely if you say chucked 2 or 100 rabbits on an island - end result is probably going to be an island full of rabbits.
- pvg 6y ago
  One Italian plumber and a bunch of rabbids, on the other hand...