Mastering AI Chip Complexity
Explore challenges and solutions in AI chip development
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SiMA.ai: Revolutionizes Edge ML Applications | Synopsys Success
SiMA.ai Enables Edge Machine Learning Applications
SiMA.ai Enables Edge Machine Learning Applications
Overview
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.
"We did our due diligence and evaluated a few computer vision processors. We looked at who could handle pre- and post-processing most effectively, with optimal performance and low power, as well as functions that would complement our ML offering. The ARC EV74 Processor met all of our criteria and, as a result, there’s a lot of handshaking between our home-grown ML IP and the ARC EV74 Processor."
Srivi Dhruvanarayan
|VP of Silicon Engineering, SiMa.ai
"As a lean startup, we don’t have the bandwidth to design all the IP ourselves or to entertain multiple vendors. We needed a single supplier for proven IP, from computer vision to PCI Express, Ethernet, memory interfaces, I2C, UARTs, security. Synopsys has it all."
Srivi Dhruvanarayan
|VP of Silicon Engineering, SiMa.ai
"In our solution, a lot of the smarts are in the software, so when software does so much heavy lifting, we rely on emulation to ensure that the software can use all of the hooks provided by the hardware. Synopsys’ ZeBu Server 4 gets us as close to the real thing as you can get. Not only does it verify the hardware, it also serves as a very good platform for software to meet hardware, for first-time-right results. So, we were able to solidify a lot of things on the platform before we got the actual chip."
Srivi Dhruvanarayan
|VP of Silicon Engineering, SiMa.ai
Challenges
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.
Solution
SiMa.ai leveraged Synopsys solutions to address these challenges:
- Synopsys ARC® EV74 Embedded Vision Processor: Used for real-time vision processing, providing flexibility, low cost, and low power consumption.
- Comprehensive IP Portfolio: Included IP for computer vision, PCI Express, Ethernet, memory interfaces, I2C, UARTs, and security.
- Synopsys Digital Design Family: Utilized Synopsys Design Compiler RTL synthesis, Synopsys PrimeTime® static timing analysis, Synopsys PrimePower RTL power estimation, and Synopsys Formality® equivalence checking.
- Synopsys Verification Family: Employed Synopsys Virtualizer™ virtual prototyping and Synopsys VCS® functional verification.
- Synopsys ZeBu® Server 4 Emulation System: Used for fast system verification, software bring-up, and power analysis.
- Synopsys Design Services: Assisted in closing timing on critical blocks in the design.
Results
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.
