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Mobileye’s EyeQ5 is an automotive system-on-chip designed to handle perception and sensor-fusion workloads in a vehicle’s central computer. In a 2016 interview, engineering chief Elchanan Rushinek described a chip intended to process data from more than 16 cameras and other sensors while targeting more than 12 tera-operations per second at 5 watts or less. Those figures were design-era claims, not independently measured benchmarks.
What is Mobileye EyeQ5?
EyeQ5 is an automotive system-on-chip (SoC), not a desktop GPU or a consumer development board. Mobileye designed it for advanced driver-assistance and automated-driving systems, with specialized processing hardware for perception tasks such as interpreting camera imagery and combining information from multiple sensors.
The idea behind the chip was to make vision processing a central part of the vehicle’s computing architecture. Rushinek told EE Times that central processing was “heavily around perception of the scene,” making vision processors such as EyeQ4 and EyeQ5 a natural choice. His description also distinguished between processors dedicated to particular sensors and central processors responsible for fusion and decision-making.
That distinction matters: EyeQ5 was not presented as a single general-purpose processor doing every task in the same way. Its heterogeneous design assigns different perception, neural-network and signal-processing workloads to specialized accelerator cores.
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How powerful was EyeQ5 supposed to be?
In the 2016 interview, Mobileye stated a target of more than 12 tera-operations per second (TOPS) at 5 watts or less. Rushinek also projected performance approximately 50–60 times that of EyeQ3. These are interview-era targets and projections; they should not be read as independent benchmark results or as a current specification for every EyeQ5 deployment.
The power target reflects the chip’s intended role in a vehicle: substantial perception processing within a low-power automotive system. The figures describe Mobileye’s stated design goals, not a directly comparable measure to a retail graphics card or a general-purpose processor.
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How many cameras and other sensors can EyeQ5 process?
Mobileye’s 2016 design description said EyeQ5 could support more than 16 sensors using 16 virtual MIPI channels. Rushinek explained that several physical sensors could be multiplexed onto a single virtual channel. The sensor inputs envisioned included cameras as well as radar and lidar; the stated count was not a promise that every vehicle configuration would use the same sensors or channel arrangement.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteRushinek described the intended coverage as 360 degrees using cameras and ultrasonic sensors, with some of that coverage also provided by radar or lidar. In that architecture, sensor-specific processors could work alongside central processors that fuse the resulting data and support decision-making.
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What do PMA and VMP do in EyeQ5?
PMA means Programmable Macro Array, and VMP means Vector Microcode Processor. Rushinek said these EyeQ5 cores could run deep neural networks efficiently and support dense-resolution sensors, including cameras and next-generation lidar and radar.
In practical terms, PMA and VMP are specialized, programmable accelerators rather than interchangeable versions of a conventional CPU core. Mobileye said it had developed the accelerators in-house over more than 15 years, co-designing their hardware, programming model and toolchain alongside its ADAS applications and SoCs. That co-design helps explain why the interview emphasized multiple programming models: different workloads are intended to run on hardware suited to them.
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Is EyeQ5 used in production vehicles?
Mobileye’s later descriptions move beyond the 2016 design targets. In a 2022 filing, the company characterized EyeQ5 as central compute for fully autonomous vehicles and said it was available for commercial vehicles and operating in autonomous test vehicles. That supports a real-world deployment role, but it does not establish that every production car with Mobileye technology uses EyeQ5.
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Mobileye also described a 2021 SuperVision configuration using two EyeQ5 chips with seven long-range cameras and four parking cameras. That example demonstrates a specific camera-only system configuration; it should not be confused with the 2016 plan for a sensor mix that could include radar and lidar. The number of chips and cameras in the SuperVision example is not a universal EyeQ5 vehicle specification.
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How to read the EyeQ5 claims by date
| When | What Mobileye described | How to interpret it |
|---|---|---|
| 2016 interview | More than 12 TOPS at 5 W or less; a projection of roughly 50–60 times EyeQ3 performance; support for more than 16 sensors through 16 virtual MIPI channels. | Design targets, projections and intended input capacity stated at the time—not independent test results or guaranteed configurations. |
| 2021 SuperVision description | Two EyeQ5 chips, seven long-range cameras and four parking cameras in the described system. | A specific demonstrated configuration, not a specification for every vehicle using EyeQ5. |
| 2022 company filing | EyeQ5 described as central compute for fully autonomous vehicles, available for commercial vehicles and operating in autonomous test vehicles. | Later company statements about availability and use; they do not supply a new benchmark for the 2016 power or performance targets. |
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