Author's Latest Posts


A Performance Analysis Of The First Generation Of HPC‐Optimized Arm Processors


In this paper, the authors present performance results from Isambard, the first production supercomputer to be based on Arm CPUs that have been optimized specifically for HPC. Isambard is the first Cray XC50 “Scout” system, combining Cavium ThunderX2 Arm‐based CPUs with Cray's Aries interconnect. The full Isambard system contained over 10,000 Arm cores. In this work, we present node‐lev... » read more

An Introduction To The ARMv8-M Architecture


The ARMv8-M architecture is used for the next-generation ARMv8-M processor family of real-time deterministic embedded processors. It is aimed at low cost deeply embedded systems, where low-latency interrupt processing is vital. The ARMv8-M architecture reduces the complexity of developing secure embedded solutions that scale all the way from the smallest IoT device to complex SoCs. ARM uses ... » read more

Fused: Closed-Loop Performance And Energy Simulation Of Embedded Systems


Energy-driven computing is an emerging paradigm that aims to fuel the proliferation of tiny and low-cost IoT sensing and monitoring devices. Energy-driven computers are generally powered by energy harvesting sources, and adapt their operation at runtime according to energy availability; thus, they must be designed and tested according to the expected dynamics of their power source. However, tod... » read more

Data Will Swamp The Internet, Unless We Think Differently


To harvest the IoT device and data opportunity in the coming years, companies must rethink their infrastructure strategy. This means re-imagining computing from the edge to the cloud. Download this report to see how leading teams are transforming their infrastructure strategies today to win tomorrow. Click here to read more. » read more

Powering The Edge


On-device machine learning (ML) is a phenomenon that has exploded in popularity. Smart devices that are able to make independent decisions, acting on locally generated data, are hailed as the future of compute for consumer devices: on-device processing slashes latency; increases reliability and safety; boosts privacy and security...all while saving on power and cost. Although ML in edge d... » read more

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

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