Eyeq4 Datasheet -

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.

The EyeQ4 is a high-performance, low-power SoC that enables advanced driver-assistance systems (ADAS) and autonomous driving applications. It is designed to process multiple sensor inputs, including cameras, radar, and lidar, and provide a comprehensive view of the environment.

: Can process information from up to 8 cameras simultaneously at 36 frames per second (fps). EyeQ4 Variants

datasheet lies in its heterogeneous architecture, which uses different types of proprietary accelerators for specific vision tasks:

Compute and Performance

Up to 8 concurrent image sensors at 36 Frames Per Second (FPS)

The EyeQ4's powerful processing underpins a comprehensive suite of driver-assistance features.

The EyeQ4 datasheet highlights several next-generation ADAS capabilities:

Features four CPU cores capable of executing four hardware threads each (totaling 16 threads). These cores handle upper-level system tasks, application scheduling, interface buses, and general driving policy tracking. Vision and Deep Learning Accelerators eyeq4 datasheet

Comprehensive Guide to the Mobileye EyeQ4 Datasheet: Specifications, Architecture, and Performance

4 multi-threaded MIPS InterAptiv cores (4 hardware threads each)

The EyeQ4 datasheet is a detailed document that outlines the technical specifications, architecture, and features of the EyeQ4 SoC. It provides an overview of the chip's design, including its processing power, memory, and interfaces. The datasheet also covers the EyeQ4's key features, such as its computer vision capabilities, machine learning algorithms, and support for various sensors and interfaces.

The EyeQ4 is a milestone in Mobileye's processor lineup. Introduced in 2015 and mass-produced in 2018, it was engineered to bridge the gap between advanced driver assistance (ADAS) and full autonomy (L3). A key innovation is its heterogeneous computing architecture, which combines multiple specialized processor types to maximize visual data processing efficiency while keeping power consumption minimal. The EyeQ4 is a family of system-on-chip (SoC)

Rather than relying on a generalized GPU, Mobileye designed a . The datasheet maps hardware sub-blocks to highly distinct mathematical functions, maximizing performance per watt.

Mobileye EyeQ4 is a high-performance System-on-Chip (SoC) designed for vision-based Advanced Driver Assistance Systems (ADAS) and semi-autonomous driving. Launched as a significant leap over its predecessor, the EyeQ3, it provides the computational "super-computer" power required for complex environmental modeling while maintaining strict automotive power efficiency. Core Specifications & Architecture Performance: Delivers over 2.5 Teraflops (2.5 TOPS) of compute power. Power Consumption: Highly efficient, typically consuming only Process Technology: Manufactured using STMicroelectronics' 28nm FD-SOI (Fully Depleted Silicon-on-Insulator) process. Heterogeneous Processor Mix: Four multi-threaded MIPS processor cores. VMP (Vector Microcode Processors):

The EyeQ4 datasheet highlights several key features that make this SoC an ideal solution for ADAS and autonomous driving applications. Some of the notable features include:

For engineers: Do not expect to bit-bang the EyeQ4 like a GPU. Its power lies in the tightly coupled hardware accelerators and Mobileye’s closed software stack. While the full datasheet remains behind legal agreements, the public specifications confirm that the EyeQ4 hit a sweet spot between cost, power, and capability—one that still powers millions of vehicles on the road today. It is designed to process multiple sensor inputs,