SiMa.ai develops a software-centric platform utilizing its proprietary Machine Learning System on Chip (MLSoC) technology to enable efficient deployment of multimodal AI applications at the edge. This platform addresses the need for high-performance, power-efficient solutions that can scale across various edge devices and applications, significantly improving processing speed and energy consumption.
Funding
$270.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
MCFounders
Product
Problem
Deploying AI applications at the edge requires high-performance, power-efficient solutions that can handle multimodal data inputs and scale across various devices. Existing solutions often struggle to deliver the necessary processing speed and energy efficiency for real-world edge deployments.
Solution
SiMa.ai offers a software-centric platform based on its proprietary Machine Learning System on Chip (MLSoC) technology, enabling efficient deployment of multimodal AI applications at the edge. The ONE Platform supports a wide range of edge applications, networks, models, modalities, frameworks, and sensors. The MLSoC architecture delivers high performance and power efficiency, outperforming traditional PCIe ML accelerators. SiMa.ai's solution allows developers to scale and deploy AI at the embedded edge, supporting applications such as smart vision, autonomous vehicles, industrial robotics, healthcare, drones, and government sector solutions.
Target Audience
The primary target audience includes developers and organizations in the automotive, industrial, robotics, healthcare, government, retail, and drone sectors looking to deploy high-performance, power-efficient AI solutions at the edge.
Features
- MLSoC (Machine Learning System on Chip) architecture for optimized edge AI processing
- ONE Platform supports any edge application, network, model, modality, framework, sensor, and resolution
- Palette Software: No-code computer vision pipeline evaluation and iteration
- MLSoC DevKit for rapid evaluation, prototyping, and demonstration of computer vision edge ML applications
- Support for Edge AI, LLMs, LMMs, CNNs, and Transformers
- 10x performance improvement compared to PCIe ML accelerators
- 40%-400% better FPS/watt compared to competitors