Brunswick’s autonomous docking technology on display at the CES consumer electronics show in Las Vegas – Copyright AFP Indranil MUKHERJEE
computers that control driverless cars They are part of the automotive technology wave, however there is an environmental concern. These computers could be a trigger for rising global carbon emissions.
New MIT research finds that if autonomous vehicles are widely adopted, hardware efficiency will need to simultaneously advance rapidly to keep computing-related emissions in check.
This is based on a model that quantifies the emissions generated by computers in fully autonomous vehicles. This finds that if self-driving cars are widely adopted, then their emissions will rival those generated by all the data centers in the world today.
To simply keep emissions at or below those levels would require hardware efficiency to improve faster than its current rate. According to some projections, 95 percent of the global vehicle fleet will be autonomous by 2050.
A further complication is that the computers driving autonomous vehicles today will not be the same systems when autonomous vehicles become commonplace. Computing workloads typically double every three years; this means that hardware efficiency would have to double faster than every 1.1 years to keep emissions below those levels.
The world’s data centers that house the physical computing infrastructure used to run applications account for about 0.3 percent of global greenhouse gas emissions (this is as much as carbon as the country of Argentina produces annually).
To draw the parallel with autonomous vehicles, the researchers determined that 1 billion autonomous vehicles, each driving for an hour per day with a computer that draws 840 watts, would consume enough energy to generate roughly the same amount of emissions as hubs. data currently.
By running various scenarios, the researchers determined that to prevent autonomous vehicle emissions from exceeding current data center emissions, each vehicle must use less than 1.2 kilowatts of power for computing.
The model developed to show this is a function of the number of vehicles in the global fleet, the power of each computer in each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity powering each computer.
The data set was very complex. For example, if an autonomous vehicle has 10 deep neural networks processing images from 10 cameras, and that vehicle drives for one hour a day, it will make 21.6 million inferences each day. One billion vehicles would make 21.6 quadrillion inferences. To evaluate the vast data set, an algorithm called multitasking deep neural network was used.
He research appears in micro IEEE and is entitled “Wheels: Emissions from On-Board Computing from Autonomous Vehicles”.
