Judging from pitches we’ve heard so far from chip suppliers such as Nvidia, Mobileye and NXP, their conceptions of an autonomous car platform (and how they plan to get there) tend to diverge. As long as everyone’s jockeying for market position by leveraging what they already have and what they think can beat the others, that’s understandable.
However, it’s important to remember that the challenges facing OEMs and tier ones are the same: a growing number of ECUs; a variety of sensors piling into autonomous cars; sensory data that need to be processed, analyzed and fused; and security — the pot of gold for connected cars. Then, there are still evolving factors such as advanced vision processing, deep learning and mapping that will affect processing power demanded in the new system architecture.
So, here’s the $64 million question. Do carmakers and tier ones today already know their autonomous car system architecture in 2020?
They don’t. At least, not yet, Eric Baissus, CEO of Kalray, told EE Times, in a recent interview here.
That’s why Kalray, a Grenoble-based startup, believes it has a good chance to move its Massively Parallel Processor Array (MPPA) processor featuring 288 VLIW cores into the market.
Kalray’s background is in extreme computing originally designed for nuclear bomb simulations at the CEA, Atomic Energy Commission, based in Grenoble, France. Today, Kalray is focused on the critical embedded market (aerospace); and cloud computing.
In Baissus’ mind, self-driving cars fall into the critical embedded market, because they absorb a lot of data coming in from external and internal parts of a vehicle, process it fast and then proceed to make quick decisions.
Baissus said that the automobile industry needs “a new generation of processors that will have the ability to handle multi-domain function integration and perform processing tasks at an extremely high level.”