AI Toolkit for Vitis AI
The latest AI toolkit for Vitis™ AI version 1.1 was used to compile the deep learning models for running accelerated inference, making this solution very cost-effective.
AMD machine learning (ML) inference enables the early detection of critical ailments by identifying anomalies in X-rays, Ultrasound, digital pathology, dermatology, ophthalmology, and more. Other applications include surgical tool guidance, drug discovery, and genome analysis. AMD and its partner ecosystem can deliver significant advancements across a wide array of healthcare applications and design methodologies.
Integrated Edge-to-Cloud Solutions
Healthcare IoT is rapidly accelerating the opportunity for cloud-connected clinical, diagnostic, and radiological equipment. Hospital administrators, IT, service providers, and medical equipment makers realize the benefits and understand the need for integrated edge-to-cloud solutions that will accelerate their time-to-market.
Reference Design Kit
AMD, Spline.ai, and AWS IoT services have a fully-functional Healthcare AI reference design kit, and an example X-Ray detection model with incredibly high accuracy and low output latency, running on the Zynq™ UltraScale+™ MPSoC integrated on the ZCU104 platform as an Edge device. They are developed using PYNQ™, an open-source Python programming platform for the AMD Zynq architecture, and the AWS Lambda function that makes this integration easily adaptable for other clinical platforms.
CNN Acceleration
The AMD deep learning processing unit integrated into the MPSoC accelerates the convolutional neural network (CNN) within the AWS IoT Greengrass. High performance at the edge combined with cloud scalability enables this solution to be available anywhere as a clinical or as a point-of-care (POC) solution. The solution can also be easily integrated with any existing healthcare applications at a large scale as a federated learning platform.
Vitis AI Platform
Vitis AI is the development platform for AI inference on AMD hardware platforms. It consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind—to help unleash the full potential of AI acceleration on AMD FPGAs and adaptive SoCs.
AMD has developed a complete end-to-end flow, allowing software developers, hardware developers, and data scientists to leverage the existing machine learning ecosystem. In this paradigm, we have designed tools to enable customers to directly parse the model graph and trained weights saved from popular ML frameworks.
PYNQ – Python on Zynq
Python powered edge analytics and ML is enabled by the "PYNQ" platform. PYNQ is a software-hardware framework for AMD Zynq adaptive SoCs. It leverages the programmable hardware to pre-process sensors and other data types to make software analysis highly efficient in an embedded processor. The PYNQ platform supports all major Python libraries like Numpy, Scikit-Learn, Pandas, and others.
E-Book Offer
High-Performance and Adaptive Computing in Healthcare
New advances in technology are helping revolutionize the healthcare landscape. Download the eBook to uncover key industry challenges and learn how technology creates new ways to treat and diagnose diseases, power research, and enable doctors to perform surgeries with greater precision.
Resources
Solution Provider |
Description |
Supported Devices |
Spline.ai |
Zynq UltraScale+ ZCU104 |
|
Amazon Web Services (AWS) |
Zynq UltraScale+ ZCU104 |
Solution Provider |
Description |
Supported Devices |
AMD - Vitis |
All AMD Platforms |
|
AMD - Vitis AI |
Adaptable and Real-Time AI Inference Acceleration |
All AMD Platforms |
AMD - PYNQ |
Zynq UltraScale+ Zynq 7000 |
|
AWS IoT |
Zynq UltraScale+ Zynq 7000 |
|
AMD for Healthcare |
Smart Solutions for Healthcare: Imaging, Diagnostics, and Clinical Equipment |
All AMD Platforms |
2D Endoscopy Multi-Class Segmentation
Dataset: EDD2020
Model: AMD custom Feature Pyramid Network with ResNet18 feature extractor and multiple prediction heads
Image: Results image from our algorithm
Model: Download
Accuracy: Dice = 80.45%, F2-score=79.15%
Performance: ZCU102 79ms latency, 40fps
Vitis™ AI Skin Lesion Classification Tutorial
Dataset: HAM10000
The Skin Lesion Tutorial
Model: View on Github
Webinar Series: Analytics to X-Rays Unchained
During this two-part webinar series, we will address the importance of in situ, in silico, inference for Healthcare.
AMD Unleashes the Power of AI in Medical Imaging
The use of AI, including ML and deep learning techniques, is poised to become a transformational force in medical imaging.
Get in Touch
Contact an AMD sales representative.