I'm not an expert in these systems, and their announcement materials don't make a bunch of stuff clear, but my attempt at summarizing for a layperson would be:

I'm not an expert in these systems, and their announcement materials don't make a bunch of stuff clear, but my attempt at summarizing for a layperson would be: - Current computers represent values as patterns of binary values. Analog computers sometimes represent values as e.g. voltages within a range. SNNs represent values with the timing of "spike" events. You could think of this like if you and your friends arranged a scheme to communicate using only the original Yo app, just by varying timing and frequency. - Current computers move values back and forth between memory and the CPU, and has very little room to hold data "close" to the CPU. Suppose your e-reader can only have one doc page open at a time; if you need to figure something out by closely comparing values between several docs, you might spend more time opening/closing/finding/paging through docs than actually reading them or thinking. These systems put (some) memory to be held by the same physical components which are doing the computations, so they aren't moving stuff back and forth in the same way. - However, current computers are quiet general in what they can do. In exchange for speed an efficiency, these systems are not general purpose. For example, b/c they have 1.15B "neurons" and 128B "synapses", any value can only (directly) influence on average 112 other values. You could think of this a bit like if Excel only let your formula reference a capped number of cells. - But for research problems which fit these constraints, this system can be extremely fast and use less power than a normal computer would.