This paper introduces a novel design of an artificial
neural network tailored for wafer-scale integration. The
presented VLSI implementation includes continuous-time analog
neurons with up to 16k inputs. A novel interconnection and
routing scheme allows the mapping of a multitude of network
models derived from biology on the VLSI neural network
while maintaining a high resource usage. A single 20 cm
wafer contains about 60 million synapses. The implemented
neurons are highly accelerated compared to biological real time.
The power consumption of the dense interconnection network
providing the necessary communication bandwidth is a critical
aspect of the system integration. A novel asynchronous lowvoltage
signaling scheme is presented that makes the wafer-scale
approach feasible by limiting the total power consumption while
simultaneously providing a flexible, programmable network
topology. |
