Author's Latest Posts


A Sneak Peek Into SVE And VLA Programming


Download this white paper to get an overview of SVE, get information on the new registers and the new instructions, and learn about the Vector Length Agnostic (VLA) programming technique, including some examples. The Scalable Vector Extension (SVE) is an extension of the ARMv8-A A64 instruction set, recently announced by ARM. Following the announcement at Hot Chips 28, a few articles describ... » read more

How to Manage One Trillion Devices on the Edge


THE EDGE, THE DATACENTER, AND NEW DESIGN PRINCIPLES: The world of compute is changing rapidly, as is the traditional view of a physical building, or buildings filled with servers, storage, and networking to “run the business”. Cloud computing, distributed cloud computing, and edge computing will all be fed by a 5G access network, forcing IT organizations to think and plan differently. Th... » read more

Building Quantum Espresso With Arm Compiler


This resource topic addresses how to build Quantum Espresso with Arm Compiler for HPC. Quantum Espresso is an integrated suite of open-source computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials. Click here to read more. » read more

Armv8.5-A Memory Tagging Extension


The Internet worm of 1988 took offline one tenth of the fledgling network, and severely slowed down the remainder [1]. Over 30 years later, two of the most important classes of security vulnerability in code written in C-like languages are still violations of memory safety. According to a 2019 BlueHat presentation, 70% of all security issues addressed in Microsoft products are caused by violati... » read more

What’s Powering Artificial Intelligence?


While artificial intelligence (AI) and machine learning (ML) applications soar in popularity, many organizations are questioning where ML workloads should be performed. Should they be done on a central processor (CPU), a graphics processor (GPU), or a neural processor (NPU)? The choice most teams are making today will surprise you. To scale artificial intelligence (AI) and machine learning (... » read more

Powering The Edge: Driving Optimal Performance With the Arm ML Processor


On-device machine learning (ML) processing is already happening in more than 4 billion smart phones. As the adoption of connected devices continues to grow exponentially, the resulting data explosion means cloud processing could soon become an expensive and high-latency luxury. The Arm ML processor is defining the future of ML inference at the edge, allowing smart devices to make independent... » read more

What’s Powering Artificial Intelligence


To scale artificial intelligence (AI) and machine learning (ML), hardware and software developers must enable AI/ML performance across a vast array of devices. This requires balancing the need for functionality alongside security, affordability, complexity and general compute needs. Fortunately, there’s a solution hiding in plain sight. To read more, click here (scroll down to "Download No... » read more

The Power Of Virtual Prototyping


As embedded SoCs continue to become more powerful and complex, beating the market may very well rely on non-traditional approaches to product design and development. Software-based methodologies involving virtual prototypes are helping to prove out designs earlier and enable companies to parallelize hardware and software development. Click here to download PDF. » read more

Machine Learning On Arm Cortex-M Microcontrollers


Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing Neural Network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. CMSIS-NN is an open-source library of... » read more

Machine Learning on Arm Cortex-M Microcontrollers


Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing Neural Network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. CMSIS-NN is an open-source library of... » read more

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