Hi, thank you for your valuable articles. I think this is one of reconfigurable computing approaches (or simply FPGA based computings, and or Adaptive Computing from DARPA). And curbon nano-wire based PLD (nanoPLD) researched by Prof. Andre DeHon and the memoristor based computing researched by hp have common scense or face to common direction. Difference between them is probably how many states held by memory element (cross-point, nano-wire may be only one bit, but memoristor may hold multi-bit).
Xbar network is the key that behaves both of the memory and interconnect.
I request you to report not only the structure (or architecture) but also application and HOW TO PROGRAM at initial state or HOW TO CONFIGURE at initial state. Currently FPGA support JTAG based programming, but it is no scense.
And I think this is solution for application-specific computing that means application is statically mapped to the device and does not change to other application (does not make context-switch). Of course, forcing the switching is possible, but it breaks data flow on device, this means a cost involving extra time and space if switching is taken.
Memristors are just a part of the solution. IBM, Hewlett Packard, HRL and their university partners are all looking at these other issues too. Look for a flurry of results over the next 6 to 18 months.
Interesting. However, even though simulating a synapse or a neuron is nice but what is even more important is the ability to physically link those neurons to hundreds or thousands of other neurons in the network. Oftentimes, the neurons can be very distant. Memristors do not provide a solution for this problem.
Also, synaptic learning in the sensory cortex is not based on the concurrency of the spikes but on afferent signals arriving about 10 milliseconds before the post-synaptic action potential. See the work of Henry Markram for more on this.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Are the design challenges the same as with embedded systems, but with a little developer- and IT-skills added in? What do engineers need to know? Rick Merritt talks with two experts about the tools and best options for designing IoT devices in 2016. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.