AI is charting new ways to become out of touch, potentially. Maybe the frame of mind around agile, sometimes spontaneous, software development that had been going on in decentralized organizations before AI took over, is coming into conflict with the mindset needed to feed AI systems with a constant high-volume flow of clean, well-structured data. This suggestion was broached by Sylvain Duranton, senior partner at Boston Consulting Group, in a recent TED Talk. "For the last 10 years, many companies have been trying to become less bureaucratic, to have fewer central rules and procedures, more autonomy for their local teams to be more agile. And now they are pushing artificial intelligence, AI, unaware that cool technology might make them more bureaucratic than ever," he stated in a recent account in Forbes.

Deep learning has continued to drive the computing industry's agenda in 2016. But come 2017, experts say the Artificial Intelligence community will intensify its demand for higher performance and more power efficient "inference" engines for deep neural networks. The current deep learning system leverages advances in large computation power to define network, big data sets for training, and access to the large computing system to accomplish its goal. Unfortunately, the efficient execution of this learning is not so easy on embedded systems (i.e. This problem leaves wide open the possibility for innovation of technologies that can put deep neural network power into end devices.

The key to this new tool is that N2D2 doesn't just compare different hardware on the basis of recognition accuracy. It can compare hardware in terms of "processing time, hardware cost, and energy consumption." This is critical, said Duranton, because different applications for deep learning will likely require different parameters in various hardware implementations. The N2D2 offers benchmarking on a variety of commercial off-the-shelf hardware -- including multi/many-core CPUs, GPUs and FPGA. Barriers to edge computing As a research organization, CEA has been studying how best to bring deep neural networks to edge computing.