Eyeq4 Datasheet (2025)
For engineers reading an eyeq4 datasheet, the following electrical and physical parameters are critical:
| Parameter | Specification | | :--- | :--- | | Process Technology | 28nm CMOS (FinFET) | | Maximum Camera Inputs | 8 simultaneous cameras | | Processing Performance | 2.5 TOPS (Trillion Operations Per Second) | | Power Consumption | 3W – 5W (typical thermal design power) | | Operating Temperature | -40°C to +125°C (Automotive Grade) | | Safety Certification | ASIL-B (ISO 26262) | | Package Type | BGA (Ball Grid Array) – 585 pin variant | | Interface Support | CAN-FD, FlexRay, Gigabit Ethernet, LVDS, I2C, SPI, GPIO |
In the rapidly evolving landscape of autonomous driving, the brain behind the perception system is just as critical as the sensors themselves. For over a decade, Mobileye (an Intel company) has dominated the market for vision-based advanced driver-assistance systems (ADAS). Among their most successful and widely deployed products is the EyeQ4. eyeq4 datasheet
For hardware engineers, system integrators, and automotive developers, the EyeQ4 datasheet is the foundational document. It provides the electrical specifications, thermal limits, pinout diagrams, and performance benchmarks necessary to integrate this powerful SoC into an Electronic Control Unit (ECU).
This article serves as a comprehensive breakdown of what you need to know about the EyeQ4 datasheet—covering its architectural features, key technical specifications, and why this chip remains a benchmark in the automotive industry. For engineers reading an eyeq4 datasheet , the
Disclaimer: The following information is aggregated from public technical disclosures, Mobileye/Intel whitepapers, and industry analysis. The full, confidential datasheet is typically available only under Non-Disclosure Agreement (NDA) to qualified automotive partners.
Unlike commodity chips (e.g., from TI or STMicroelectronics), Mobileye does not publish the full EyeQ4 datasheet on its public website. To obtain it: Unlike commodity chips (e
While the raw TOPS number (2.5) seems low compared to modern desktop GPUs, the datasheet emphasizes efficiency. The EyeQ4 executes 8-bit integer CNN inferences at a rate of 0.25ms per layer. This allows it to detect objects (cars, pedestrians, traffic signs) at 30-36 frames per second across three distinct camera streams simultaneously.