SiMa.ai is at the forefront of ushering in an age of effortless machine learning (ML) for the embedded edge. With its team of software, semiconductor design, and machine learning experts, SiMa.ai aims to disrupt the $10T+ embedded edge market by replacing decades-old technology with a purpose-built, software-first platform that scales ML at the embedded edge. The company recently achieved first-silicon success for its Machine Learning System-on-Chip (MLSoC) platform, which uses Synopsys design, verification, IP, and design services solutions.

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As SiMa.ai integrates machine learning at the embedded edge, it faces several challenges:

  • Centralized Computing Limitations: Addressing capacity, energy use, and cost challenges.
  • Latency and Network Dependency: Reducing real-time latency requirements and alleviating the need for a network connection.
  • Security and Compliance: Keeping data local for better security and compliance.


SiMa.ai leveraged Synopsys solutions to address these challenges:


The collaboration between SiMa.ai and Synopsys yielded significant benefits:

  • Effortless ML for Embedded Edge: SiMa.ai's MLSoC platform runs any computer vision application, network, model, framework, and sensor at any resolution.
  • High Performance and Low Power: The MLSoC platform brings superior performance and the lowest power consumption by integrating ML into the SoC from the beginning.
  • Enhanced Productivity: The Synopsys ARC EV74 Processor and other IP cores optimized pre- and post-processing, complementing SiMa.ai's ML offering.
  • Comprehensive Verification: Synopsys ZeBu Server 4 enabled solidification of the platform before obtaining the actual chip, ensuring first-time-right results.

By seamlessly integrating ML into its SoC, SiMa.ai is transforming how intelligence can be integrated into an array of ML devices used in computer vision applications for the embedded edge. The expertise available from Synopsys helps AI hardware pioneers like SiMa.ai bring their revolutionary concepts to life and make an impact in the ML-driven embedded edge space.