The automotive industry as you may be aware is very much in the business of providing more ADAS (Automotive Driver Assist Systems) features in todays’ vehicles. This is to:
- Meet the increasing demanding (European) NCAP safety ratings for cars
- Safety features sells cars
- To differentiate one manufacturer from another
The ADAS devices today are becoming ever more complex and capable to the point they are likely to make up most of the future autonomous vehicles’ systems. In any domain where safety is a priority the development of such systems must meet functional safety specifications as well as technical requirements. The safety assurances asked for during the development of such systems include how those systems operate in their environments.
The automotive industry is changing. In the past they have been conservative in their approach to development of vehicles’ systems, often years behind the curve. This is because it takes a large amount of time to verify and re-verify systems are safe. Until recently one approach to safety was to reduce as much as possible the complexity of the systems where ever possible. Today in order to meet the challenge of providing semi-autonomous driver aids by 2025 they are gravitating to solutions that are increasingly complex. Also, such solutions are being developed outside the realm of safety, outside of the automotive’s traditional chain manufacturing; OEMs, Tier 1, Tier 2 and smaller manufacturers. The growth in software complexity has impart be due to adopting Artificial Intelligence (AI) components to form part of their solutions.
As you may be aware AI solutions involve many layers of software – the CNN (compute neural network) software stack. The interactions between the layers is complex as state, data, commands, kernels and results are spawned and move between layers to and from the hardware.
For the automotive systems of the future to be safe it is another example of where the work carried out by the LPGPU2 project can be applied beyond the traditional mobile domain. Understanding the power usage and performance of the hardware and how the software interacts on top is vitally important where vehicles’ ECUs are generally low power passively cooled units.
The integration of complex software with hardware while meeting tight development constraints is a challenge for all automotive companies. For functional safety engineers the scope of concerns with an expanding code base supporting diverse hardware is immense. As Khronos moves open standards APIs such as OpenCL(TM) and SYCL(TM) to be ISO 26262 compatible, automotive manufacturers are looking to address the gap by working with researchers and companies to meet the demand.
At the Autonomous Vehicle Test & Development Symposium 2018, Codeplay presents a talk on the LPGPU2 CodeXL profiling tool for CNN using Tensorflowâ„¢ as a use case. The presentation gives a holistic overview of the compute stack and talks about how the tool can probe the otherwise concealed layers in the compute stack. How the tool can capture and analyse power profiles alongside the API calls made. How the tool can provide analysis and feedback on the data captured to make improvements.
If you wish to read more about the conference, go here: http://www.autonomousvehicle-software.com/en/
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