The Python community can strongly benefit from the immense and flexible computing power of Field Programmable Gate Arrays (FPGA). FPGAs are composed of memories, various logic and math hardware units that can be programmed and configured to accelerate any algorithm. They can be used to even create your own custom CPUs and other gadgets. FPGA chips have presently evolved into multi-faceted Systems on a Chip (SoC) which include multi-core CPUs and hardware accelerators such as embedded GPUs, video codecs, FEC and hardware interfaces such as USB. These FPGA SoCs are similar to other SoCs such as Raspberry Pi or Beaglebone boards but those lack the power of the FPGA. With the right system design the FPGA can turn them into super turbo charged machines. Many SoCs with FPGAs run full Arm Linux and can utilize popular package manager root file systems (Debian, Ubuntu, etc) as well as support for the full Python 2x and 3x environments. FPGA SoCs are being positioned to take over the edge, cloud and machine learning realms by some of the top vendors in the computing industry. What is missing is a clean cohesive means to interface Python code to harness the acceleration that the FPGA hardware can provide. This talk will outline the current state of development tools and methods for the new Ultra96 board that Avnet and Xilinx have just released. The intent is to show the community the value of the proposition and implore them to work together to achieve a more homogeneous and simplified approach to taking Python to the next level.
YVR18-311:A Call to Action: Accelerating Python with FPGAs
LVC20-201 Boot-Time Tracing With Extra Boot ConfigFriday, October 16, 2020
Boot-time tracing is one of the latest Linux kernel tracing proposal, which allows us to trace kernel booting with full tracing features, like per-event filters and triggers, histograms, instances, dynamic-events etc. Along with the boot-time tracing the kernel command-line interface is also expanded by Extra Boot Config (XBC) so that user can specify complex boot-time settings with structured-key value configuration file.
This talk will show you what the boot-time tracing and the extra boot config provide, the advantages and how you can use it for your boot-time features.
LVC20-317 Analysis of ARM64's Competence for Oil&Gas Seismic Data Processing ApplicationsWednesday, September 30, 2020
Each seismic survey in Oil & Gas exploration generates tons of seismic wave data, typically hundreds of Terabytes. Transforming the huge amount of data into a accurate earth subsurface model requires exascale level computing power. This presentation will analyze the computing requirements and trends in seismic data processing, evaluate the competence of the current generations ARM64 SoCs and the new features required.
LVC20-117 Everything you want to know about live migration on Arm64 CloudWednesday, September 30, 2020
Slack channel to chat with the speaker during the live broadcast: https://linaroconnect.slack.com/archives/C01B1SV18F5
Currently, one big gap between Arm64 and X86 cloud platforms is that X86 can provide a much better instance migration experience than the Arm64 platform. CPU comparison and CPU model capabilities have provided Arm64 VM with the ability to live migration among different hardware vendors. This function is the essential function of the data center. From the cloud management framework, we also need to consider the realization of supporting VM live migration.
In this session, we will talk about what we have done in the most widely used virtualization management tool - Libvirt to provide better live migration capabilities on Arm64 platform and also some details in the newest lightweight cloud management project such as Kubevirt.
With live migration support on Arm64, it can finally benefit the cloud ecosystem for large scale datacenter scenarios which may use different Arm64 CPU architectures and vendors.
LVC20-303 State of Big Data and Data Science on ARMFriday, September 25, 2020
From being called the 'new gold / oil', Big data technologies from its peak hype cycle have now matured as quintessential technology that powers many of today’s businesses and enterprises. Most companies have changed themselves from being data-generating to data-powered, making use of actionable data and business insights. Big data is not just one piece of technology (Hadoop or Spark), rather it is a huge ecosystem, an assembly line of technologies and processes. There has been an increase in focus on data science and machine learning as a logical next step of evolution of Big Data. Data Strategy governs the quality of the data which acts as a precursor to data science and machine learning.
In this talk, we will look at where ARM stands in regards to having big data systems running in production. We will look at various achievements in the last few years and also understand where we stand currently in the process of making ARM as the first class citizen. We will also cover some of the pain points we have faced and what is important to work on in the near future and beyond.
Please join if you would like to know the status of Linaro’s BDDS team and its roadmap.
BKK19-502 - Autoware on ARM ImprovementsTuesday, April 16, 2019
As members of the Autoware foundation it is Linaro’s hope to make improvements to Autoware’s code base, promote Automotive applications running on the ARM ecosystem, and foster the open source community while also providing good code support, practice, and standards.
In this session we will talk about the difference between Autoware.ai and Autoware.Auto and the transition from ROS1 to ROS2 for Real Time applications. We will also mention Linaro’s efforts to make improvements to Autoware’s code and provide unit tests for the various Autoware modules. Finally we will talk about our efforts of getting Autoware running efficiently on ARM platforms.
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