SiMa.ai is a software-centric, embedded edge machine learning system-on-chip company that develops a complete MLSoC platform combining hardware and software to enable high-performance, low-power machine learning inference on embedded edge devices. The platform supports traditional computing with ML inference, delivers push-button deployment and scaling at the edge, and adapts to various frameworks, networks, models, sensors, and modalities. It targets applications from computer vision to generative AI, offering improvements in performance and energy efficiency while enabling easy integration and secure operation for edge deployments.
SiMa.ai is a software-centric, embedded edge machine learning system-on-chip company that develops a complete MLSoC platform combining hardware and software to enable high-performance, low-power machine learning inference on embedded edge devices. The platform supports traditional computing with ML inference, delivers push-button deployment and scaling at the edge, and adapts to various frameworks, networks, models, sensors, and modalities. It targets applications from computer vision to generative AI, offering improvements in performance and energy efficiency while enabling easy integration and secure operation for edge deployments.
SiMa.ai is a software-centric, embedded edge machine learning system-on-chip company that develops a complete MLSoC platform combining hardware and software to enable high-performance, low-power machine learning inference on embedded edge devices. The platform supports traditional computing with ML inference, delivers push-button deployment and scaling at the edge, and adapts to various frameworks, networks, models, sensors, and modalities. It targets applications from computer vision to generative AI, offering improvements in performance and energy efficiency while enabling easy integration and secure operation for edge deployments.
SiMa.ai is a software-centric, embedded edge machine learning system-on-chip company that develops a complete MLSoC platform combining hardware and software to enable high-performance, low-power machine learning inference on embedded edge devices. The platform supports traditional computing with ML inference, delivers push-button deployment and scaling at the edge, and adapts to various frameworks, networks, models, sensors, and modalities. It targets applications from computer vision to generative AI, offering improvements in performance and energy efficiency while enabling easy integration and secure operation for edge deployments.