At SID Display Week 2019, Arm officially launches the Arm Mali -D77 DPU display processor that significantly improves the VR user experience with dedicated hardware functions for VR HMDs, namely: Lens Distortion Correction (LDC), Chromatic Aberration Correction (CAC) and Asynchronous Timewarp (ATW). These are on top of the already feature rich Mali-D71 DPU for premium mobile devices, Mali-D77 changes the way we think about VR workload distribution across the SoC. It enables a significant step-up in the display resolutions and frame rates that can be achieved within the power constraints of mobile VR HMD devices. This will pave the way towards lighter, smaller, more comfortable VR devices free from any cables, which, in turn, could drive the widespread adoption of consumer VR.
At SID Display Week 2019, Arm Mali -D77 DPU display processor is launched, that significantly improves the VR user experience with dedicated hardware functions for VR HMDs, namely: Lens Distortion Correction (LDC), Chromatic Aberration Correction (CAC) and Asynchronous Timewarp (ATW). These are on top of the already feature rich Mali-D71 DPU for premium mobile devices, Mali-D77 changes the way we think about VR workload distribution across the SoC. It enables a significant step-up in the display resolutions and frame rates that can be achieved within the power constraints of mobile VR HMD you can read more about the Mali-D77 here: https://community.arm.com/developer/tools-software/graphics/b/blog/posts/introducing-the-arm-mali-d77-display-processor
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.
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 is a Senior Software Developer at ARM and a developer for the EBBR project https://github.com/ARM-software/ebbr. 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
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.
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
Vector Packet Processor (VPP) Works on various ARM platforms out of the box, All CI tests pass, ARM boards getting added to Fd.io lab, CSIT under progress, Performance benchmarking/analysis under progress.