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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Mobileye’s EyeQ5 is an automotive system-on-chip designed to combine camera-based perception with data from radar and lidar in a central vehicle computer. In a 2016 EE Times interview, engineering leader Elchanan Rushinek described a chip aimed at more than 12 tera-operations per second while using 5 watts or less—a historical design target, not a current independently measured benchmark.
What is Mobileye EyeQ5?
EyeQ5 is an automotive SoC: an integrated chip intended for vehicle systems, not a desktop graphics card, consumer processor or retail development board. Mobileye designed it for automated-driving workloads, where a vehicle must interpret sensor data and combine it into a representation of what is happening around it.
The central-computer idea was important to Rushinek’s explanation. Rather than treating every sensor as an isolated input, the vehicle could use dedicated processors for certain sensors and a central processor for sensor fusion and decision-making. Rushinek said the central processing workload was heavily focused on scene perception, making vision processors a natural fit. In this design, EyeQ5 was meant to do more than process camera images alone: it was positioned to bring perception and sensor-fusion work together.
What did Mobileye’s engineering chief say about the design?
Rushinek’s 2016 account emphasized heterogeneous computing: different specialized processor families handle different kinds of perception, neural-network and signal-processing tasks. The point was not to maximize one general-purpose processor, but to match work to the hardware best suited to it.
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PMA and VMP accelerators
Two named core types were the PMA, or Programmable Macro Array, and VMP, or Vector Microcode Processor. Rushinek said they could run deep neural networks efficiently and support dense-resolution inputs from cameras as well as next-generation lidar and radar. The names describe in-house accelerator architectures; they do not refer to interchangeable consumer components.
Mobileye said it had developed these accelerators in-house for more than 15 years, co-designing them with its ADAS applications and SoCs. That work included the hardware, programming model and toolchain. This helps explain why the interview discussed multiple programming models: specialized cores need software and development tools suited to their particular jobs.
How the sensor inputs fit together
EyeQ5 was planned to accept more than 16 sensors using 16 virtual MIPI channels. Rushinek explained that multiple physical sensors could be multiplexed onto one virtual channel. Thus, “16 channels” does not mean a limit of 16 physical sensors, nor does it mean every sensor had a separate channel.
The intended vehicle architecture paired camera coverage with ultrasonic sensing, while radar and lidar could also contribute to parts of the 360-degree view. The chip’s role was to support that broader system, including fusion and decision-making; the interview did not imply that EyeQ5 itself contained the vehicle’s cameras, radar or lidar.
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How powerful was EyeQ5 supposed to be?
In 2016, Mobileye described a target of more than 12 tera-operations per second at 5 watts or less, and Rushinek projected performance around 50–60 times that of EyeQ3. These are interview-era targets and projections. They should not be read as independent laboratory measurements or as specifications guaranteed for every production configuration. “Operations” also should not be treated as a directly comparable measure of application performance without matching workload and measurement conditions.
| Claim or milestone | What was stated | How to interpret it |
|---|---|---|
| Compute and power target | More than 12 tera-operations per second at 5 W or less | Mobileye’s 2016 interview-era target, not a later independent benchmark |
| Relative performance | Approximately 50–60 times EyeQ3 | Rushinek’s 2016 projection; not an independently measured comparison |
| Sensor input design | More than 16 sensors through 16 virtual MIPI channels | 2016 design description; physical sensors could be multiplexed on a virtual channel |
| SuperVision configuration | Two EyeQ5 chips, seven long-range cameras and four parking cameras | Mobileye’s 2021 demonstration configuration, not a universal EyeQ5 system specification |
Is EyeQ5 used in production vehicles?
Later Mobileye materials provide evidence of use beyond the 2016 announcement. In a 2022 filing, the company described EyeQ5 as central compute for fully autonomous vehicles and said it was available for commercial vehicles and operating in autonomous test vehicles. That establishes the company’s stated deployment status, but it is not a fresh performance measurement.
In 2021, Mobileye also demonstrated its SuperVision system with two EyeQ5 chips, seven long-range cameras and four parking cameras. Mobileye described that particular system as camera-only. This example shows that EyeQ5 could be used in a vision-focused configuration; it does not contradict the chip’s original design ambition to support fusion with radar and lidar in other vehicle architectures.
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How to read the original EyeQ5 claims today
- Separate intent from measured results: The 2016 throughput, power and EyeQ3 comparison figures are targets or projections from the interview.
- Separate chip capability from a vehicle system: Sensor counts describe an intended input architecture, while a deployed vehicle’s sensors and processing arrangement depend on its system design.
- Distinguish examples from universal configurations: SuperVision’s 2021 two-chip camera arrangement is one demonstrated use, not a claim that every EyeQ5 deployment has the same hardware.
- Do not expect a retail chip: EyeQ5 is an OEM-integrated automotive semiconductor, not a consumer upgrade part or a general-purpose board for individual buyers.
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