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IP brings real-time deep learning to automotive systems

January 22, 2018

nitind-January 22, 2018

LONDON — German intellectual property supplier Videantis launched its sixth-generation processor IP architecture, which adds deep learning capability to a solution that combines computer vision, image processing and video coding from a single unified SoC platform.

The main application initially is to target the automotive industry, which is moving towards more sophisticated advanced driver assistance systems (ADAS) and ultimately to fully autonomous vehicles, which are dependent on multiple cameras.

The new v-MP6000UDX visual processing architecture can be configured from a single media processor core to up to 256 cores, and is configurable based on the company’s own programmable DSP architecture. Each core includes a dual-issue VLIW core, and provides eight times more multiply accumulates per core compared to its previous generation processor, which the company says results in 1000x performance improvement in deep learning applications, while maintaining software compatibility with its previous v-MP4000HDX architecture.

The v-MP6000UDX processor architecture includes an extended instruction set optimized for running convolutional neural nets (CNN), increases the multiply-accumulate throughput per core eightfold to 64 MACs per core, and extends the number of cores from typically 8 to up to 256.

The heterogeneous multicore architecture includes multiple high-throughput VLIW/SIMD media processors with a number of stream processors that accelerate bitstream packing and unpacking in video codecs. Each processor includes its own multi-channel DMA engine for efficient data movement to local, on-chip, and off-chip memories.

The v-MP6000UDX subsystem can have a single v-MP (media processor core), up to an array of 256 cores for embedded vision with deep learning. Source: Videantis
The v-MP6000UDX subsystem can have a single v-MP (media processor core), up to an array of 256 cores for embedded vision with deep learning.
Source: Videantis

 

 

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