Super Resolution refers to the process of reconstructing a higher-resolution image or sequence from the observed lower – resolution images. An image may have a “lower resolution” due to a smaller spatial resolution (i.e., size) or due to a result of degradation (such as blurring). It has a wide range of applications including but not limited to satellite imaging, medical imaging, video surveillance as well as video streaming which is the primary focus of this article.

Xilinx Add-on for MATLAB & Simulink is a single tool that unifies Model Composer and System Generator for DSP. It is a Model-Based Design tool enabling algorithm and RTL/hardware developers to rapidly design and explore within the MathWorks Simulink®  environment and target Xilinx devices.

The tool provides high-level performance-optimized blocks and validates functional correctness through system-level simulations. It also transforms algorithmic specifications to production-quality implementation and accelerates the path to production through automatic code generation.

Imagine a world where technology for humans doesn't require humans to operate. Where data is analyzed in industrial, automotive, and even medical settings by the machines themselves. And where factory line workers, long-haul truckers, and surgeons are not obsolete but more efficient and more capable than ever before.

As the nexus of human and cyber physical cognition, Machine Learning  systems are taking us into the fifth industrial revolution. And the Xilinx Vitis Unified Software Platform  is making it easier for companies to integrate Machine Learning features into their products.

The text is among the most brilliant and influential creations of humankind. The rich, precise high-level semantics embodied in the text helps understand the world around us and build autonomous-capable solutions that can be deployed in a live environment. Therefore, automatic text reading from natural environments, also known as scene text detection/recognition or PhotoOCR, has become an increasingly popular and important research topic in computer vision.

As the written form of human languages evolved, we developed thousands of unique font-families. When we add case (capitals/lower case/uni-case/small caps), skew (italic/roman),  proportion (horizontal scale), weight,  size-specific (display/text), swash, and serifization (serif/sans in super-families), the number grows in millions, and it makes text identification an exciting discipline for Machine Learning.

Regardless of the final target technology (e.g., FPGA or CPU), available resources are typically restricted. Thus, an optimized architecture and network design is highly necessary when integrating neural network-based approaches into embedded projects.

This article covers the Solectrix AI workflow, including both the proper network handling and the transfer to the chosen target technology. An example is given for an object detection task running on XILINX MPSoC technology using Vitis AI.

Cloud computing has become the new computing paradigm. For cloud computing, virtualization is necessary to enable isolation between users, high flexibility and scalability, high security, and maximized utilization of hardware resources.

Since 2017, because of the advantages of programmability, low latency, and high energy efficiency, FPGA has been widely adopted into cloud computing. Amazon Web Service, Aliyun (Alibaba Cloud), Microsoft Azure, Huawei Cloud and etc. have all provided Xilinx FPGA instances on their cloud at present. However, FPGA instances on the cloud are still physical instances which are aimed for single-task and static-workload scenario, which means if there are multiple users, they can only share one FPGA instance in a time-division multiplexing (TDM) way. There is an increasing demand for virtualized FPGA.

Is data the end goal or just the beginning? 

Globally, 10s of millions of IP cameras are installed each year.  If we assume that there 100 million IP cameras installed worldwide (which may be a conservative number) and if each of these cameras were to unintelligently stream H.264 encoded HD video at 30fps, 24/7/365, the required total bandwidth would be ~ 859Tbps, or ~3.4 Zettabytes annually.  If even half of these cameras were connected to the cloud, it suggests that IP camera internet traffic may currently account for upwards of 1/12th of the total global internet traffic.  Clearly, these are ballpark numbers, but it serves to illustrate the scale of the problem.

The use of artificial intelligence (AI) – including machine learning (ML) and deep learning techniques (DL) is poised to become a transformational force in medical imaging. Patients, healthcare service providers, hospitals, professionals, and various stakeholders in the ecosystem all stand to benefit from ML driven tools. From anatomical geometric measurements to cancer detection, the possibilities are endless. In these scenarios, ML can lead to increased operational efficiencies and generate positive outcomes. 

There’s a broad spectrum of ways that ML can be used in medical imaging. For example, radiology, dermatology, vascular diagnostics, digital pathology and ophthalmology all use standard image processing techniques.

You may not be intimately familiar with Baidu's DeepSpeech2 Automatic Speech Recognition model (Amodei et al., 2015: ) but I am willing to bet that if you are reading this, speech recognition is now part of your daily life.

The roots of ASR technology date back to the late 1940s and early 1950s.  In 1952, Bell Labs (Davis, Biddulph, Balashek) designed "AUDREY", an "Automatic Digit REcognition" device which could recognize the digits 0 – 9.  This system could be trained (tuned, actually) per user and could achieve accuracies beyond 90% for speaker-dependent recognition and ~50-60% for speaker-independent recognition.

I’d like to kick of the new year with a summary of a White Paper published by our AI partner Mipsology. The paper was written in conjunction with Dell and was recently posted on Dell’s web site . 

The Zebra Acceleration Stack from Mipsology provides CNN inference acceleration with remarkable ease. Their tools provide an easy path to migrate CNN models from GPUs to FPGAs, allowing non-FPGA experts to benefit from superior throughput and latency. When combined with Alveo data center acceleration cards and Dell EMC PowerEdge Servers, Mipsology Zebra provides a complete solution for AI Inference acceleration. The image below summarizes the Zebra Acceleration Stack, compared with a GPU stack.