UC Santa Barbara

In the search for intelligent silicon, the energy costs of traditional sensing and computation are barriers to progress and are forcing new modalities for communication, computation, and storage. Conventional signaling (discrete binary digital, analog level) suffers from non-idealities due to physical noise on large wires between miniature transistors. The errors found in the communication channels between the devices cause increased power demands because a classical computation must use enough energy to compute and transmit the answer across wires. This work combines recent advances in computation and communication, to simultaneously sense and transmit information acquired while sending the data through a spiking communication channel with additional computation capabilities.

Spiking or pulse-based asynchronous computation and communication schemes indicate additional energy bounds useful for understanding noisy answers. The use of pulse signals provide behaviorally robust and scalable system architectures for novel encoders. The encoders take advantage of hierarchical uneven fractional connectivity to transmit data during a space-time computation for the purposes of neuromorphic communication. These encoders enable semi-intelligent sensors capable of efficient data transfer from practical CMOS mixed-signal race logic integrated circuits.