Holoscan Platform

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Welcome to RidgeRun's guide to
Holoscan Platform

Holoscan Platform

NVIDIA's Holoscan is a comprehensive platform designed to accelerate the development and deployment of AI and high-performance computing (HPC) applications for real-time insights. It's particularly well-suited for industries like healthcare, automotive, and robotics, where real-time processing of vast amounts of data is crucial.

Beyond, the Holoscan Sensor Bridges (HSB) are FPGA-based boards from Lattice Semiconductor, Microchip, and NVIDIA. The boards provide low-latency sensor data processing using an FPGA interface and the Holoscan NVIDIA software. The main idea of the HoloScan is to provide the data directly into the host device's memory.

Key Components of Holoscan

Hardware: Holoscan leverages NVIDIA's powerful GPUs and other specialized hardware, like FPGAs, to handle intensive computational tasks.
Software: The Holoscan SDK provides a robust framework for developing and deploying AI applications. It includes tools for data ingestion, preprocessing, model inference, and visualization. It also supports Python, C++, and Graph Composer.

Holoscan Sensor Bridge

The Holoscan Sensor Bridge extends the capabilities of the Holoscan Platform by serving as a versatile interface for integrating diverse sensors into real-time AI applications. Sensors such as cameras, LiDAR, and medical imaging devices produce massive amounts of data that must be ingested, synchronized, and processed with minimal delay. The Sensor Bridge simplifies this process, acting as the critical link between hardware sensors and AI-driven insights. Capabilities of the Holoscan Sensor Bridge:

Sensor Integration: Supports a wide variety of sensors, including high-definition cameras, ultrasound probes, and industrial LiDAR devices. Provides out-of-the-box support for popular data transfer protocols like GMSL2, GMSL3, and Ethernet-based sensors. At the moment, it is experimental and supports cameras only.

Low-Latency Data Ingestion: Ingests high-bandwidth data streams in real-time, preserving the temporal accuracy needed for low-latency applications such as AR overlays or surgical guidance. It uses DPDK-compatible Ethernet for better latency.

Data Synchronization and Processing: Synchronizes multi-sensor inputs to ensure coherent data streams, even in complex setups with multiple devices. Allows for pre-processing operations, such as filtering, normalization, or compression, before feeding data into AI inference pipelines.

Edge Scalability: Optimized to run efficiently on NVIDIA edge platforms like the Jetson Orin and IGX Orin. It can scale from single-sensor setups to large, distributed systems with multiple inputs.

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