Category: ARM

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Arm Neoverse N1 and E1

Posted by Charbax – March 26, 2019

The Neoverse N1 CPU is optimized for a wide range of cloud native server workloads executing at a world-class compute efficiency. This enables an infrastructure transformation where processing is pushed to the edge where data is generated, thereby providing more scalability than moving all data to centralized datacenters.

The Arm Neoverse E1 CPU delivers best-in-class throughput efficiency. It incorporates a new simultaneous multithreading (SMT) microarchitecture design. With SMT, the processor can execute two threads concurrently resulting in better aggregate throughput performance.

The Neoverse E1 delivers 2.1x more compute performance, 2.7x more throughput performance and 2.4x better throughput efficiency compared to the Cortex-A53. The design is highly scalable to support throughput demands for next generation edge to core data transport.

Latest from ARM at Embedded World 2019

Posted by Charbax – March 3, 2019

Thomas Ensergueix, ARM Senior Director of Embedded, talks about the ARMv8-M processors that are now shipping, ARM Cortex-M23, and ARM Cortex-M33, implementing more and more advanced security in the Embedded market. ARM Platform Security Architecture (PSA) certification Levels being developed with their partners, and releasing Helium ARMv8.1-M for a next level Machine Learning in the ARM Microcontrollers market. At this event, ARM also with Raspberry Pi foundation celebrates the 25 million Raspberry Pi development boards that have been shipped since I filmed this video at ARM Techcon 2011 as one of the first on the web about the Raspberry Pi.

ARM Helium Armv8.1-M architecture, Machine Learning on ARM Cortex-M

Posted by Charbax – March 3, 2019

Jem Davies, ARM VP, Fellow and GM, Machine Learning Group talks about ARM's new Helium Machine Learning architecture for the ARM Cortex-M based microcontrollers, as a follow on to ARM CMSIS-NN Neural Network Kernels which Boosted Efficiency in Microcontrollers by 5x last year, now ARM launches Helium ARMv8.1-M to improve machine learning performance, with up to 50x on machine learning workloads, about 5x improvement in performance for regular DSP based workloads, as open source software and the new ARMv8.1-M architecture to be integrated in Microcontroller designs to come in the future.

Grant Likely talks EBBR bootloader for ARM

Posted by juliusaugustus – November 21, 2018

Grant likely is a Senior Software Developer at ARM and a developer for the EBBR project The EBBR or Embedded Base Boot Requirements is a specification for bootloaders for ARM based devices. This specification would enable arm based devices to share the same bootloader thus reducing development costs. This would enable the same OS to more easily boot on multiple devices

Google IoT Demo for Zephyr RTOS With TF-M Secured on Musca

Posted by Charbax – October 6, 2018

In this demo, the Trusted Firmware M is providing the SPE and JWT sign, Zephyr is providing the NSPE and The Google IoT application is running on Zephyr using secure services from Trusted Firmware M.

- Platform Security Architecture (PSA) is an IoI security framework being developed by Arm.
- Trusted Firmware M (TF-M) is an open source project to provide PSA compliant secure firmware for M profile devices.
- Zephyr is a Linux Foundation Collaboration Project to provide a small, scalable RTOS for connected, resource constrained device.
- Arm Musca-A1 subsystem based on Armv8-M which allows partitioning the SW execution in Secure and Non Secure domain.

Arm NN and the Linaro Machine Learning Initiative

Posted by Charbax – September 24, 2018

Jem Davies is the General Manager of the Machine Learning Group at Arm, he talks about the new Machine Learning Collaboration with Arm NN and Linaro, where Arm is donating the Arm NN inference engine and software developer kit (SDK) to Linaro’s Machine Intelligence Initiative. As part of this initiative – which aims to be a focal point for collaborative engineering in the ML space – Arm is also opening Arm NN to external contributions.

Linaro’s Machine Learning Initiative will initially focus on inference for Arm Cortex-A SoCs and Arm Cortex-M MCUs running Linux and Android, both for edge compute and smart devices. The team will collaborate on defining an API and modular framework for an Arm runtime inference engine architecture based on plug-ins supporting dynamic modules and optimized shared Arm compute libraries. The work will rapidly develop to support a full range of processors, including CPUs, NPUs, GPUs, and DSPs and it is expected that Arm NN will be a crucial part of this.

You can watch Jem Davies keynote at Linaro Connect here

VPP on ARM64 From Edge to Core

Posted by Charbax – September 8, 2018

Vector Packet Processor (VPP) Works on various ARM platforms out of the box, All CI tests pass, ARM boards getting added to lab, CSIT under progress, Performance benchmarking/analysis under progress.

Arm ServerReady Update with Dong Wei, Senior Director at ARM Architecture and Technology Group

Posted by Charbax – August 5, 2018

Arm ServerReady is a program to make sure that the ecosystem is enabled to support the ARM server, making sure that all the operating systems just work and can be installed without a lot of patches and stuff. They ask ODM and Silicon Providers to work with ARM to comply with the standards to make sure everything just is working. Linaro LEG also did an SBSA QEMU effort, that is well aligned with the Arm ServerReady Program letting people run the tests even before the hardware is available.

You can find the slideshow about this here:

ARM TrustZone Media Protection with OPTEE

Posted by Charbax – August 5, 2018

ARM is showing TrustZone Media Protection working with the Open Source Trusted Execution Environment, adopting everything within the Android operating system.

ARM Press Conference at Computex 2018, ARM Cortex-A76, Mali-G76, Mali-V76

Posted by Charbax – June 5, 2018

Here's my full video in 4K from my front row seat of the ARM Press Conference at Computex 2018. You can also watch my Interview with Nandan Nayampally here.