New-Tech Europe | February 2019

as of recent the main driving force in popularizing artificial intelligence. Architecture: Choosing the correct tools The application requirements and constraints are what drives the specification of the final product that incorporates an Artificial Intelligence-related algorithm. These are related to robustness, inference time, hardware resources and quality of service. This is especially when considering edge deployment and choosing appropriate the embedded platform. Robustness is the accuracy of the model's output and the ability to generalize, e.g. the likelihood of computing a correct output and avoiding overfitting. Typically, the more complex the model (or deeper, more layered) and the richer the dataset, the more robust the model tends to be. Defining a desired inference time is entirely dependant on the application. In some cases, for example in automotive, it is crucial for safety reasons to get a response from a machine vision system under a millisecond. This not the case for a sensor fusion system with slow- changing measurements where one could infer only every a minute or so. Inference speed depends on the model complexity - more layers correspond to more computations and that results in longer inference time. This can be offset by selecting and using more powerful compute resources, e.g. embedded GPUs, DSPs, Neural accelerators with OpenCL kernels to fully utilize the available resources. In addition, the model memory footprint grows with the number of neurons and weights. Each weight is a number that must be stored in memory. To reduce the size of the model, and often to address

Picture Nu1: NVIDIA Jetson Xavier System On Module and Development Kit

AI is not new to Arrow Electronics but now we believe that this is the time to drive this technology bottom-up meaning that we need to address all options available and to fit it to the customer demand and requirements. In September 2018 , Arrow Electronics and NVIDIA have signed a global agreement to bring the NVIDIA ® Jetson™ Xavier™, a first- of-its-kind computer designed for AI, robotics and edge computing, to companies worldwide to create next-generation autonomous machines. Jetson Xavier — available as a developer kit that customers can use to prototype designs — is supported by comprehensive software for building AI applications. This includes the NVIDIA JetPack™ and DeepStream SDKs, as well as CUDA ® , cuDNN and TensorRT™ software libraries. At its heart is the new NVIDIA Xavier processor, which provides more computing capability than a powerful workstation and comes in three energy-efficient operating modes. The Tech Specs for Jetson AGX Xavier is GPU 512-core Volta GPU with Tensor Cores , CPU 8-core ARM

hardware specifics, one can convert the weights from floats or doubles and use integers instead. Quality of service and reliability of the system depends on the deployment model. In a cloud-based approach, the fact that a connection is needed, can result in the system is unreliability. What happens if the server is unreachable? Still, a decision must be made. In such cases, the edge may be the only viable solution, e.g. in autonomous cars, isolated environments. It is also essential to understand that the Machine Learning-based algorithms are inherently probabilistic systems and the output is the likelihood with a certain dose of uncertainty. However, for many use cases, the accuracy or reliability of predictions made by AI systems already exceeds those made by humans. Whether the system designer should consider a 90% or 99% probability to be high enough depends on the application and its requirements. Finally, when considering an appropriate hardware and software, a designer should realize that the difficulty of development and scalability of certain solutions may differ.

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