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A Layered Approach To High Performance Device Virtualization


The complexity and performance requirements of computing systems have been growing and demands are further driven by applications, such as ML and the everything-connected world of IoT with many billions of connected devices. Arm has developed a virtualization and accelerator strategy to address this, which we discuss in this white paper from our Architecture and Technology Group A layered... » read more

Powering The Edge: Driving Optimal Performance With Ethos-N77 Processor


Repurposing a CPU, GPU, or DSP is an easy way to add ML capabilities to an edge device. However, where responsiveness or power efficiency is critical, a dedicated Neural Processing Unit (NPU) may be the best solution. In this paper, we describe how the Arm Ethos-N77 NPU delivers optimal performance. Click here to read more. » read more

The Power Of Virtual Prototyping: From SoC Design To Software Development


Virtual prototypes and hardware design: More powerful and complex integrated circuits and System-on-Chip (SoC) designers have a daunting task at both the hardware and software level. SoC architects need a method for early evaluation of hardware components, known as Intellectual Property (IP) blocks, that will have direct impact on the commercial success of the SoC. There are a range of complex ... » read more

Powering The Edge: Driving Optimal Performance With Ethos-N77 Processor


Repurposing a CPU, GPU, or DSP is an easy way to add ML capabilities to an edge device. However, where responsiveness or power efficiency is critical, a dedicated Neural Processing Unit (NPU) may be the best solution. In this paper, we describe how the Arm Ethos-N77 NPU delivers optimal performance. Click here to immediately download the paper. » read more

Deploying Accurate Always-On Face Unlock


Accurate face verification has long been considered a challenge due to the number of variables, ranging from lighting to pose and facial expression. This white paper looks at a new approach — combining classic and modern machine learning (deep learning) techniques — that achieves 98.36% accuracy, running efficiently on Arm ML-optimized platforms, and addressing key security issues such a... » read more

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

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