SiMa.ai

The First Purpose Built Gen AI at the Edge MLSoC

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Description

SiMa.ai provides advanced solutions for edge artificial intelligence, centered around its innovative Machine Learning System on Chip (MLSoC™). This platform is engineered to enable full-pipeline implementations of real-world machine learning applications directly on edge devices. It focuses on delivering significant improvements in both performance and power efficiency for demanding AI workloads. The company's technology is designed to support a wide array of edge applications, networks, models, modalities, frameworks, sensors, and resolutions, aiming to simplify the deployment and scaling of AI in embedded systems.

The SiMa.ai ecosystem includes the Palette™ software and MLSoC™ DevKit 2.0, facilitating rapid evaluation, prototyping, and demonstration of computer vision and other edge ML applications. Tools like Edgematic™ allow for no-code computer vision pipeline evaluation, which helps accelerate time to deployment. SiMa.ai's solutions cater to various sectors by enabling powerful on-device processing, addressing challenging industrial tasks, enhancing autonomous vehicle capabilities, and improving operational efficiency in areas like smart retail and drones through substantial gains in processing speed and power conservation.

Key Features

MLSoC™ (Machine Learning System on Chip): Purpose-built silicon for full-pipeline ML solutions at the edge.
Palette™ Software: Comprehensive software platform for developing and deploying edge ML applications.
Edgematic™: No-code computer vision pipeline evaluation and iteration tool for faster time to deployment.
High Performance: Delivers up to 10x performance and 12x faster end-to-end pipeline FPS than comparable PCIe ML accelerators.
Power Efficiency: Achieves 40%-400% better Frames Per Second per watt (FPS/watt) compared to competitors.
Versatile Platform Support: Compatible with any edge application, network, model, modality, framework, sensor, and resolution.
MLSoC™ DevKit 2.0: Development kit for rapid evaluation, prototyping, and demonstration of computer vision edge ML applications.

Use Cases

Industrial Sector & Robotics: Addressing challenging industrial applications and improving operational performance.
Autonomous Vehicles: Expediting the development and deployment of autonomous driving and electrification roadmaps.
Government Sector: Providing powerful, Size, Weight, and Power (SWaP) friendly, and future-proof AI solutions.
Healthcare: Enabling powerful on-device processing of medical data with low power utilization for edge medical devices.
Smart Retail: Delivering real-time insights on consumer behavior and store operations at the edge.
Drones: Achieving significantly increased flight time and improved responsiveness through efficient on-board processing.
Smart Vision Systems: Deploying highly-efficient, low-power computer vision capabilities across various applications.