The hard problem is to provide processor performance scalability in a post-Moore world. We are tackling this by event based, near-memory, sparse computing. Trai

The hard problem is to provide processor performance scalability in a post-Moore world. We are tackling this by event based, near-memory, sparse computing. Traits that can be just as well attributed to the brain. The challenge here is that a whole new set of algorithms is required. A good illustration for that are DVS (Dynamic Vision Sensors). They produce a continuous stream of asynchronous events, just like our eyes, and algorithms like object detection have to work on this stream of events rather than a simple sequence of frames. The advantages are ultra-low latency, high energy efficient computing. Only what needs to be computed is computed, asynchronously. Since DVS is not a very widespread technology (yet) we do frame-to-event conversion to tap into the energy efficiency of sparse, near-memory compute. All of this processing can be done on the same architecture, since it can be explicitly programmed to do e.g. frame-to-event conversion, and trained to do inference on objects in the resulting stream.