Recently, there has been a huge interest in Internet of Things (IoT) systems, which bring the digital world into the physical world around us. However, barriers still remain to realizing the dream applications of IoT. One of the biggest challenges in building IoT systems is the huge diversity of their demands and constraints (size, energy, latency, throughput, etc.). For example, virtual reality and gaming applications require multiple gigabits-per-second throughput and millisecond latency.
Deep neural networks are pushing computer designs into new regimes of performance. They need much more than what CPUs provide, and their demand has grown faster than the growth in chip capability that Moore’s Law provides. Because of the compute they require, both training and inference have been a new and valuable market for the GPU. Yet while GPUs do the job better than CPUs, the GPU is not optimized for neural networks, and new, better adapted architectures are now appearing.
Reaching an inflection point: How healthcare will become accessible and affordable for every person on Earth