Artificial Intelligence (AI) will disrupt a large breath of markets and transform organizations, institutions and societies. AI is expected to bring profound positive impacts, as well as the risks and possible pitfalls. The European Union member states recently signed a Declaration of cooperation on AI to ensure the EU’s competitiveness in this field and deal with possible challenges arising from it. The European Commission is also boosting funding in support of AI with the aim of increasing overall investment in it to at least 20 billion Euro’s by 2020. For countries, it is of paramount importance to be ready to reap the benefits of AI.
Artificial Intelligence (AI) is a resource hog. AI-powered programs will grind to a halt unless developers continue to seek out the fastest, most scalable, most power-efficient and lowest-cost hardware, software and Cloud platforms to run their workloads.
As the AI arena shifts toward workload-optimized architectures, there’s a growing need for standard benchmarking frameworks to help practitioners assess which target hardware/software stacks are best suited for training, inferencing, and other workloads.
In the past year, the AI industry has moved rapidly to develop open, transparent, and vendor-agnostic frameworks for benchmarking for evaluating the comparative performance of different hardware/software stacks in the running of diverse workloads. Here the most important of these initiatives, as judged by the degree of industry participation, the breadth of their missions, the range of target hardware/software environments they’re including in their scope, and their progress in putting together useful frameworks for benchmarking today’s top AI challenges.