Eyeq4 Datasheet ((exclusive)) Review
How it handles for PCB layout design.
| Feature | EyeQ3 | | EyeQ5 | | :--- | :--- | :--- | :--- | | Launch Year | 2014 | 2018 | 2021 | | Process Node | 40nm | 28nm | 7nm | | Performance (TOPS) | 0.3 | 2.5 | 24 | | Camera Inputs | 2 | 8 | 20+ | | Target Autonomy | L2 | L2 / L3 | L4 / L5 |
The EyeQ4 architecture is built on a (Fully Depleted Silicon On Insulator) process technology from STMicroelectronics , which is critical for its high efficiency. Specification Details Performance Over 2.5 Teraflops (TFLOPS) or 2.5 TOPS Power Consumption Approximately 3 Watts for typical automotive use Video Processing
The EyeQ4 is a family of system-on-chip (SoC) processors developed for automotive vision applications, designed to meet the demands of advanced driver-assistance systems (ADAS) and the scaling requirements of automated driving. Built to process high-resolution camera inputs with low latency and to run complex perception, sensor-fusion, and neural-network workloads, EyeQ4 represents a generation of automotive-grade vision accelerators that bridge camera sensors and higher-level vehicle behavior.
This technical guide unpacks the architectural blocks, peripheral interfaces, pin layouts, and application standards defined across the EyeQ4 processor family variants. Hardware Architecture and Compute Subsystem eyeq4 datasheet
: Powers technologies like Autonomous Emergency Braking (AEB), next-generation lane detection, and vehicle detection from any angle.
: Supports Mobileye's Road Experience Management (REM) for high-definition mapping and localization.
Capable of processing up to 8 cameras simultaneously at 36 frames per second (fps). Architectural Overview
The CPU complex executes Mobileye's RSS model—a mathematical framework that defines what it means for an automated vehicle to drive safely. It evaluates surrounding traffic behaviors to prevent the host vehicle from initiating dangerous maneuvers. Deep Learning Object Classification How it handles for PCB layout design
The is a powerful System-on-Chip (SoC) designed for Level 2+ (L2+) and Level 3 (L3) autonomous driving. It is the fourth generation of Mobileye’s EyeQ family, balancing high performance with low power consumption. It processes camera, radar, and LiDAR data simultaneously.
For engineers reading an , the following electrical and physical parameters are critical:
Memory, I/O, and Sensor Interfaces
: Classifying every pixel in a video frame (e.g., identifying road vs. sidewalk vs. obstacles). Built to process high-resolution camera inputs with low
: Designed to meet Automotive Safety Integrity Level (ASIL) requirements, featuring hardware-level redundancy, error-correcting code (ECC) on internal memory blocks, and built-in self-tests (BIST).
: Extremely efficient, operating within a budget of approximately 3 Watts. Core Configuration : 4 multi-threaded MIPS InterAptiv CPU cores.
Yields a 10x raw performance jump over EyeQ3 with only a minor 20% increase in power draw . Physical Package Pinout
The foundational magic of the EyeQ4 datasheet lies in its highly heterogeneous, many-core computing architecture. Rather than relying on a power-hungry general-purpose GPU, Mobileye engineered a proprietary mix of general-purpose RISC processor cores and highly specialized hardware accelerators. 1. General-Purpose Compute Layer Mobileye EyeQ4 Vision Processor Family - Yole Group