{"id":9531,"date":"2021-06-25T16:12:23","date_gmt":"2021-06-25T14:12:23","guid":{"rendered":"https:\/\/future-markets-magazine.com\/?p=9531"},"modified":"2024-07-03T14:38:30","modified_gmt":"2024-07-03T12:38:30","slug":"ai-and-machine-learning-xilinx","status":"publish","type":"post","link":"https:\/\/future-markets-magazine.com\/en\/electronics-semiconductor-industry-en\/ai-and-machine-learning-xilinx\/","title":{"rendered":"AI and Machine Learning"},"content":{"rendered":"

Artificial ­intelligence (AI), including machine learning<\/a> (ML) and deep-learning techniques (DL), is poised to become a transformational force in healthcare. The various stakeholders in the ecosystem all stand to benefit from ML-driven tools. From anatomical geometric measurements to cancer detection, radiology, surgery, drug discovery and genomics, the possibilities are endless. ML can lead to increased operational efficiencies, extremely positive outcomes and significant cost reduction.<\/b><\/p>\n

Opportunities for machine learning in healthcare<\/h2>\n

There is a broad spectrum of ways that ML can be used to solve critical healthcare problems. For example, digital pathology, radiology, dermatology, vascular diagnostics and ophthalmology all use standard image-processing techniques. <\/span><\/p>\n

Chest X-rays are the most common radiological procedure, with over two billion scans performed worldwide every year. Amounting to 548,000 scans a day. Such a huge quantity of scans imposes a heavy load on radiologists and taxes the efficiency of the workflow. Methods involving ML, deep neural networks (DNN) and convolutional neural networks (CNN) often outperform radiologists in speed and accuracy. Although the expertise of a radiologist is still of paramount importance. However, under stressful conditions during a fast decision-making process, human error rate could be as high as 30 %. Aiding the decision-making process with ML methods can improve the quality of the result. Furthermore, it can also provide radiologists and other specialists as an additional tool.<\/span><\/p>\n

Machine learning on the test bench<\/h2>\n

Validations of ML are today coming from multiple and very reliable sources. In a study by Stanford ML Group, a 121-layer CNN was trained to detect pneumonia better than four radiologists. <\/span>I<\/span>n multiple other studies by the National Institute of Health, attempts at early detection, using a DNN-model, achieved better accuracy. Even better than multiple radiologists’<\/span> diagnoses at the same time – from malignant pulmonary nodules to diagnosing lung-cancer.<\/p>\n

Many procedures within radiology, pathology, dermatology, vascular diagnostics and ophthalmology could involve large image sizes requiring complex image processing. Also, the ML workflow can be computing- and memory-intensive. The predominant computation is linear algebra and demands many computations and a multitude of parameters. <\/span><\/p>\n

This results in billions of multiply-accumulate (MAC) operations, hundreds of megabytes of parameter data. Moreover, it requires a multitude of operators and a highly-distributed memory subsystem. So, performing accurate image-inference<\/a>s efficiently for tissue detection or classification using traditional computational methods on PCs and GPUs is inefficient. Accordingly, healthcare companies are looking for alternative techniques to address this problem.<\/span><\/p>\n

Improved efficiency with ACAP devices<\/h2>\n

Xilinx technology offers a heterogeneous and highly distributed architecture to solve this problem for healthcare companies. The Xilinx Versal Adaptive Compute Acceleration Platform (ACAP) family of system-on-chip<\/a>s <\/span>(SoCs). This SoCs featuring adaptable field-programmable gate arrays (FPGA<\/a>s), integrated digital-signal processor<\/span>s (DSPs) and accelerators for deep learning<\/a>. Additionally, <\/span>SIMD VLIW engines with highly distributed local memory architectures and multi-processor systems, is known for its ability to perform massively. Parallel signal processing of high-speed data in close to real time.<\/span><\/p>\n

Versal ACAP has multi-terabit-per-second Network-on-Chip (NoC) interconnect capability and an advanced AI-Engine containing hundreds of tightly <\/span>integrated VLIW SIMD processors. This means computing capacity can be moved beyond 100 tera operations per second (TOPS).<\/span><\/p>\n

These device capabilities dramatically improve the efficiency of how complex healthcare ML algorithm<\/a>s are solved. And help to significantly accelerate healthcare applications at the edge, all with fewer resources, less cost and power. With Versal ACAP devices, support for recurrent networks could be inherent due to the simple nature of the architecture. And its supporting libraries.<\/span><\/p>\n

Xilinx has an innovative ecosystem for algorithm<\/a> and application developers. Unified software platforms mean developers can use advanced devices – such as ACAPs – in their projects. Such as Vitis for application development and Vitis AI for optimising and deploying accelerated ML inference<\/a>.<\/p>\n

Healthcare and medical-device workflows are undergoing major changes. In the future, medical-workflows <\/span>will be “big data<\/a>” enterprises with significantly higher requirements for computational needs, security, and <\/span>patient safety. Distributed, non-linear,<\/span> parallel and heterogeneous computing platforms are key for solving and managing this complexity. Xilinx devices like Versal and the Vitis software platform are ideal for delivering the optimised AI architectures of the future.<\/span><\/p>\n

 <\/p>\n

Discover more about Xilinx: www.xilinx.com.<\/a><\/strong><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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