A throughput-on-demand address-event transmitter for neuromorphic chips

I present a scalable 2-D address-event transmitter interface designed to take advantage of the high integration, densities available with, advanced submicron technology. To sustain throughput, it exploits the linear increase in the number of active neurons per row with as-ray size, instead of counting on a linear increase in the unit-current/unit-capacitance ratio, as existing designs do. I synthesize an asynchronous implementation starting from a high-level specification, and present test results from a 104 x 96-neuron chip fabricated in a 1.2 mu m CMOS process. Reading out the state of all neurons in a selected row in parallel, and sending their spikes in a tight burst of events, yields cycle times between. 40 to 70ns-six to ten times shorter than the 420ns minimum cycle time reported in earlier work.