Cadence Expands Tensilica Vision Family with Radar Accelerator and New DSPs for Automotive Applications

By Tiera Oliver

Associate Editor

Embedded Computing Design

March 11, 2024

News

Cadence Expands Tensilica Vision Family with Radar Accelerator and New DSPs for Automotive Applications

Cadence Design Systems, Inc. expanded its Tensilica IP portfolio to address the increasing computational requirements associated with automotive sensor fusion applications. The new high-performance Cadence Tensilica Vision 331 DSP and Vision 341 DSP combine vision, radar, lidar and AI processing in a single DSP for multi-modal, sensor-based system designs, delivering energy efficiency in the small area.

Per the company, when paired with the new Cadence Tensilica Vision 4DR Accelerator optimized for 4D imaging radar applications, customers can achieve even improved radar performance and energy efficiency

The new Vision 341 DSP and Vision 331 DSPs combine the Tensilica ConnX and Vision instruction-set architectures to offer SoC providers for automotive, drone, robotics, and autonomous vehicle systems a programmable, single-DSP solution for image sensing, radar, lidar and AI workloads. Other features and benefits include:

  • The 1024-bit Vision 341 DSP provides 2X multiply-accumulate (MAC) capability compared to the Vision 331 DSP while delivering ideal performance and energy efficiency compared to GPUs or CPUs.
  • For certain 4D imaging radar workloads, the 512-bit Vision 331 DSP offers up to 4X performance improvement over the Vision 230 DSP in radar boost mode, while the Vision 341 DSP offers up to 6X performance improvement compared to the Vision 230 DSP.
  • When paired with the new DSPs for 4D imaging radar applications, the Vision 4DR accelerator offers 3X greater performance and up to 6X greater performance/area advantage compared to a Vision 341 DSP alone and 5X greater performance compared to a Vision 331 DSP alone.
  • The new Vision DSPs deliver up to 2X computer vision performance improvements for computer vision filter and NMS algorithms, as well as AI improvements for quantization and depth-wise separable convolution.

The Vision 341 and Vision 331 DSPs support the Tensilica Instruction Extension (TIE) language, allowing customers to customize the instruction set. The Cadence NeuroWeave Software Development Kit (SDK) provides neural network support for both DSPs. In addition, the Vision 341 and Vision 331 DSPs support more than 1700 OpenCV-based vision library functions, SLAM Library, Point Cloud Library (PCL), Radar library, Nature DSP library, OpenCL and the Halide compiler for computer vision, imaging, radar and lidar applications. Both cores are automotive ready with ASIL-B hardware random faults and ASIL-D systematic fault certification.

“With the growing prevalence of vision transformers, automotive SoC and systems companies need best-in-class performance for sensor fusion applications. Increasingly, they are seeking a highly flexible single-DSP solution for computer vision, radar and lidar with AI processing,” said David Glasco, VP of R&D for the Silicon Solutions Group at Cadence. “As the leading provider of vision and radar DSP IP, we’re extremely well positioned to capitalize on this growing need by offering our customers the best of both worlds in a single DSP with optional radar acceleration for emerging 4D radar applications.”

For more information, visit: https://www.cadence.com/go/vision-331-pr or https://www.cadence.com/go/vision-341-pr. More information on the Vision 4DR accelerator can be found at https://www.cadence.com/go/vision-4dr-accelerator-pr

Tiera Oliver, Associate Editor for Embedded Computing Design, is responsible for web content edits, product news, and constructing stories. She also assists with newsletter updates as well as contributing and editing content for ECD podcasts and the ECD YouTube channel. Before working at ECD, Tiera graduated from Northern Arizona University where she received her B.S. in journalism and political science and worked as a news reporter for the university’s student led newspaper, The Lumberjack.

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