Autonomous Vehicles: Memory Requirements & Deep Neural Net Limitations

By _Rambus Blog

July 22, 2019

Introduction

According to the National Highway Traffic Safety Administration (NHTSA), various driver assistance technologies are already helping to save lives and prevent injuries. Specific examples include helping drivers avoid making unsafe lane changes, warning of other vehicles when backing up, or automatically braking when a vehicle ahead stops or slows abruptly. The above-mentioned safety technology leverages a wide range of hardware – including sensors, cameras and radar – to help vehicles identify safety risks and warn drivers to avoid crashes and collisions.

Self-Driving Memory Requirements

However, many of these systems are using DRAM-based memory solutions with relatively modest bandwidths. This is because most advanced driver-assistance systems (ADAS) are rated Level 2 and only offer drivers partial automation capabilities. Future self-driving cars (Level 3 – Level 5) will require new generations of memory with significantly increased bandwidths. This additional bandwidth will enable autonomous vehicles to rapidly execute massive calculations and safely implement real-time decisions on roads and highways, with the ultimate goal of doing this without any driver assistance.

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