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Best-in-Class R-Car V3U ASIL D System-on-Chip for Automated Driving

Package Information

Pkg. Type:FCBGA
Pkg. Code:
Lead Count (#):1992
Pkg. Dimensions (mm):
Pitch (mm):0.8

Environmental & Export Classifications

ECCN (US)3A991.a.2
HTS (US)8542.31.0070
Pb (Lead) Free
Moisture Sensitivity Level (MSL)

Product Attributes

Lead Count (#)1992
3D GPUGE7800 600MHz
Accelerator4x ISP, 6x IMR, 2x DOF, 2x STV, 4x ACF, 2x SMD, 2x DSP
Application Core8 x CA761, 8GHz/93k DMIPS
Audio CodecNo
Auto I/F8x CAN 2.0B/FD, Flexray
CPUCortex-A76 x 8, Cortex-R52 x 2
Computer Vision / Frequency8CVe / 533MHz
DDR Interface128-bit LPDDR4-4266
Deep Learning (TOPS)66 TOPS
Ethernet6x Gbit AVB
Flash Interface2x QSPI, 1x eMMC
High Speed4x PCIe 4.0
Highest ASIL LevelUp to ASIL D
Key Features4x UART, 6x SPI, 7x I2C, Security, JTAG
Parametric ApplicationsADAS/AD
Pitch (mm)0.8
Pkg. TypeFCBGA
Real Time Core Freq / KDMIPSCR52 Lockstep, 1GHz, 2k DMIPS
Video CodecH.264 Enc, H.264 Codec
Video Input4x MIPI-CSI2
Video Output2x MIPI CSI2, 2x DU

Description

The R-Car V3U SoC, based on the R-Car Gen 4 architecture, is the latest member of the open and innovative Renesas autonomy platform for ADAS and AD. The platform offers scalability from entry-level NCAP applications up to highly automated driving systems with the R-Car V3U providing up to 96k DMIPS and 60TOPS. The SoC integrates multiple sophisticated safety mechanisms that provide high coverage with fast detection and response for random hardware faults, achieving ASIL D metrics for the majority of the SoC processing chain, as well as reducing design complexity, time to market and system cost. It delivers highly flexible DNN (Deep Neural Processing) and AI machine learning functions. Its flexible architecture can handle any state-of-the-art neural networks providing up to 60 TOPS with low power consumption allowing air cooling systems. The R-Car V3U comes with an open and integrated development environment that enables customers to take advantage of the R-Car platform’s built-in hardware benefits, as well as low power consumption and deterministic real-time software to enable fast time-to-market for computer vision and deep learning-based solutions.